Release notes

This is the 0.23.0 release of TensorFlow Probability. It is tested and stable against TensorFlow 2.15.0 and JAX 0.4.20 .

Change notes

[coming soon]

Huge thanks to all the contributors to this release!

• Christopher Suter
• Colin Carroll
• Jacob Burnim
• Juan Martinez
• Sergei Lebedev
• Sophia Gu
• Srinivas Vasudevan

Release notes

This is the 0.22.1 release of TensorFlow Probability. It is tested and stable against TensorFlow 2.14.0 and JAX 0.4.16 and 0.4.19 .

Change notes

See the release note for TFP 0.22.0 at https://github.com/tensorflow/probability/releases/tag/v0.22.0 .

Fixes some NumPy deprecation warnings by no longer casting size-1 arrays to ints.

Dependency typing_extensions is no longer pinned to <4.6.0.

Support for Python 3.8 has been removed starting with TensorFlow Probability 0.22.0.

Huge thanks to all the contributors to this release!

• Brian Patton
• Colin Carroll
• Du Phan
• Emily Fertig
• Fiona Lang
• Frederik Gossen
• Gabriel Rasskin
• Haotian Chen
• Jacob Burnim
• Jake VanderPlas
• Mark McDonald
• Oskar Fernlund
• Pavel Sountsov
• Richard Levasseur
• Salman Faroz
• Sergei Lebedev
• Srinivas Vasudevan
• Thomas Colthurst
• Urs Köster
• Yu Feng

Release notes

This is the 0.22 release of TensorFlow Probability. It is tested and stable against TensorFlow 2.14.0 and JAX 0.4.16 .

Change notes

Support for Python 3.8 has been removed starting with TensorFlow Probability 0.22.0.

[Coming soon.]

Huge thanks to all the contributors to this release!

• Brian Patton
• Colin Carroll
• Du Phan
• Emily Fertig
• Fiona Lang
• Frederik Gossen
• Gabriel Rasskin
• Haotian Chen
• Jacob Burnim
• Jake VanderPlas
• Mark McDonald
• Oskar Fernlund
• Pavel Sountsov
• Richard Levasseur
• Salman Faroz
• Srinivas Vasudevan
• Thomas Colthurst
• Urs Köster
• Yu Feng

Release notes

This is the 0.21.0 release of TensorFlow Probability. It is tested and stable against TensorFlow version 2.13 and JAX 0.4.14 .

Change notes

[no major changes]

Huge thanks to all the contributors to this release!

• bjp
• chansoo
• colcarroll
• emilyaf
• feyu
• flang
• Jacob Burnim
• jburnim
• jcater
• juanantoniomc
• Matthew Feickert
• oskarfernlund
• phawkins
• schwartzedward
• siege
• Srinivas Vasudevan
• ursk

Release notes

This is the 0.20 release of TensorFlow Probability. It is tested and stable against TensorFlow version 2.12 and JAX 0.4.8 .

Change notes

• Add LinearOperatorBasis and LinearOperatorRowBlock.
• Ensure Dirichlet and RelaxedOneHotCategorical transform correctly under bijectors.
• Add SphericalSpace and use in all Spherical Distributions
• Add GeneralSpace.transform_general
• Fix guitar numpy rewrite_equivalence_test.
• BREAKING CHANGE: Ignore deprecated always_yield_multivariante_normal arg to tfd.GaussianProcess and tfd.GaussianProcessRegressionModel so that event shape is always [1] for a single index point.
• Create a bayesopt submodule of TFP experimental and add acquisition functions.
• Add the FeatureScaledWithCategorical kernel, a PSD kernel over structures of continuous and categorical data, to TFP experimental.
• [BREAKING] Remove deprecated arg BDF.use_pfor_to_compute_jacobian.

Huge thanks to all the contributors to this release!

• ashishenoy
• atondwal
• bjp
• Christopher Suter
• colcarroll
• Colin Carroll
• emilyaf
• fdtomasi
• flang
• Jacob Burnim
• jburnim
• jcater
• juanantoniomc
• langmore
• Leandro Campos
• leben
• Matthew Feickert
• mmladenov
• nkovela
• Pavel Sountsov
• phandu
• phawkins
• power
• S. Amin
• siege
• Srinivas Vasudevan
• synandi
• thomaswc
• Tirumalesh
• ujaved
• ursk

Release notes

This is the 0.19.0 release of TensorFlow Probability. It is tested and stable against TensorFlow version 2.11 and JAX 0.3.25 .

Change notes

• Bijectors

  • Added UnitVector bijector to map to the unit sphere.

• Distributions

  • Added noncentral Chi2 distribution to TFP.
  • Added differentiable quantile and cdf function approximation to NC2 distribution.
  • Added quantiles to Student-T, Beta and SigmoidBeta, with efficient implementations for Student-T quantile/cdf.
  • Allow structured index points to GaussianProcess* classes.
  • Improved efficiency of GaussianProcess* gradients through custom gradients on log_prob.

• Linear Algebra

  • Added functions (with custom gradients) to handle Hermitian Symmetric Positive-definite matrices:
    • tfp.math.hspd_logdet
    • tfp.math.hpsd_quadratic_form_solve and tfp.math.hpsd_quadratic_form_solvevec
    • tfp.math.hpsd_solve and tfp.math.hpsd_solvevec

• Optimizer

  • BUGFIX: Prevent Hager-Zhang linesearch from terminating early.

• PSD Kernels

  • Added support for structured inputs in PSD Kernel.

• STS

  • Added seasonality support to STS Gibbs Sampler.

• Other

  • BUGFIX: Allow jnp.bfloat16 arrays to be correctly recognized as floats.

Huge thanks to all the contributors to this release!

• Brian Patton
• Chen Qian
• Christopher Suter
• Colin Carrol
• Emily Fertig
• Francois Chollet
• Ian Langmore
• Jacob Burnim
• Jonas Eschle
• Kyle Loveless
• Leandro Campos
• Du Phan
• Pavel Sountsov
• Sebastian Nowozin
• Srinivas Vasudevan
• Thomas Colthurst
• Umer Javed
• Urs Koster
• Yash Katariya

Release notes

This is the 0.18.0 release of TensorFlow Probability. It is tested and stable against TensorFlow version 2.10 and JAX 0.3.17 .

Change notes

[coming soon]

Huge thanks to all the contributors to this release!

[coming soon]

Release notes

This is the 0.17.0 release of TensorFlow Probability. It is tested and stable against TensorFlow version 2.9.1 and JAX 0.3.13 .

Change notes

• Distributions

  • Discrete distributions transform correctly when a bijector is applied.
  • Fix bug in Taylor approximation of log-normalizing constant for the ContinuousBernoulli.
  • Add TwoPieceNormal distribution and reparameterize it's samples.
  • Make IncrementLogProb a proper tfd.Distribution.
  • Add quantiles to Empirical distribution.
  • Add tfp.experimental.distributions.MultiTaskGaussianProcessRegressionModel
  • Improve efficiency of MultiTaskGaussian Processes in the presence of observation noise: Reduce complexity from O((NT)^3) to O(N^3 + T^3) where N is the number of data points and T is the number of tasks.
  • Improve efficiency of VariationalGaussianProcess.
  • Add tfd.LognNormal.experimental_from_mean_variance.

• Bijectors

  • Fix Softfloor bijector to act as the identity at high temperature, and floor at low temperature.
  • Remove tfb.Ordered bijector and finite_nondiscrete flags in Distributions.

• Math

  • Add tfp.math.betainc and gradients with respect to all parameters.

• STS

  • Several bug fixes and performance improvements to tfp.experimental.sts_gibbs for Gibbs sampling Bayesian structural time series models with sparse linear regression.
  • Enable tfp.experimental.sts_gibbs under JAX

• Experimental

  • Ensemble Kalman filter is now efficient in the case of ensemble size << observation size and an "easy to invert" modeled observation covariance.
  • Add a perturbed_observations option to ensemble_kalman_filter_log_marginal_likelihood.
  • Add Experimental support for custom JAX PRNGs.

• Other

  • Add assertAllMeansClose to tfp.TestCase for testing sampling code.

Huge thanks to all the contributors to this release!

• Adam Sorrenti
• Alexey Radul
• Christopher Suter
• Colin Carroll
• Du Phan
• Emily Fertig
• Fabien Hertschuh
• Faizan Muhammad
• Francois Chollet
• Ian Langmore
• Jacob Burnim
• Jake VanderPlas
• Kathy Wu
• Kristian Hartikainen
• Kyle Loveless
• Leandro Campos
• Xinle Sheila Liu
• ltsaprounis
• Matt Hoffman
• Manas Mohanty
• Max Jiang
• Pavel Sountsov
• Peter Hawkins
• Praveen Narayan
• Renu Patel
• Ryan Russell
• Scott Zhu
• Sergey Lebedev
• Sharad Vikram
• Srinivas Vasudevan
• tagoma
• Urs Koster
• Vaidotas Simkus
• Vishnuvardhan Janapati
• Yilei Yang

Release notes

This is the 0.16.0 release of TensorFlow Probability. It is tested and stable against TensorFlow version 2.8.0 and JAX 0.3.0 .

Change notes

[coming soon]

Huge thanks to all the contributors to this release!

• Alexey Radul
• Ben Lee
• Billy Lamberta
• Brian Patton
• Chansoo Lee
• Christopher Suter
• Colin Carroll
• Dave Moore
• Du Phan
• Emily Fertig
• François Chollet
• Gianluigi Silvestri
• Jacob Burnim
• Jake Taylor
• Junpeng Lao
• Matthew Johnson
• Michael Weiss
• Pavel Sountsov
• Peter Hawkins
• Rebecca Chen
• Sharad Vikram
• Soo Sung
• Srinivas Vasudevan
• Urs Köster

Release notes

This is the 0.15 release of TensorFlow Probability. It is tested and stable against TensorFlow version 2.7.0.

Change notes

• Distributions

  • Add tfd.StudentTProcessRegressionModel.
  • Distributions' statistics now all have batch shape matching the Distribution itself.
  • JointDistributionCoroutine no longer requires Root when sample_shape==().
  • Support sample_distributions from autobatched joint distributions.
  • Expose mask argument to support missing observations in HMM log probs.
  • BetaBinomial.log_prob is more accurate when all trials succeed.
  • Support broadcast batch shapes in MixtureSameFamily.
  • Add cholesky_fn argument to GaussianProcess, GaussianProcessRegressionModel, and SchurComplement.
  • Add staticmethod for precomputing GPRM for more efficient inference in TensorFlow.
  • Add GaussianProcess.posterior_predictive.

• Bijectors

  • Bijectors parameterized by distinct tf.Variables no longer register as ==.
  • BREAKING CHANGE: Remove deprecated AffineScalar bijector. Please use tfb.Shift(shift)(tfb.Scale(scale)) instead.
  • BREAKING CHANGE: Remove deprecated Affine and AffineLinearOperator bijectors.

• PSD kernels

  • Add tfp.math.psd_kernels.ChangePoint.
  • Add slicing support for PositiveSemidefiniteKernel.
  • Add inverse_length_scale parameter to kernels.
  • Add parameter_properties to PSDKernel along with automated batch shape inference.

• VI

  • Add support for importance-weighted variational objectives.
  • Support arbitrary distribution types in tfp.experimental.vi.build_factored_surrogate_posterior.

• STS

  • Support + syntax for summing StructuralTimeSeries models.

• Math

  • Enable JAX/NumPy backends for tfp.math.ode.
  • Allow returning auxiliary information from tfp.math.value_and_gradient.

• Experimental

  • Speedup to experimental.mcmc windowed samplers.
  • Support unbiased gradients through particle filtering via stop-gradient resampling.
  • ensemble_kalman_filter_log_marginal_likelihood (log evidence) computation added to tfe.sequential.
  • Add experimental joint-distribution layers library.
  • Delete tfp.experimental.distributions.JointDensityCoroutine.
  • Add experimental special functions for high-precision computation on a TPU.
  • Add custom log-prob ratio for IncrementLogProb.
  • Use foldl in no_pivot_ldl instead of while_loop.

• Other

  • TFP should now support numpy 1.20+.
  • BREAKING CHANGE: Stock unpacking seeds when splitting in JAX.

Huge thanks to all the contributors to this release!

• 8bitmp3
• adriencorenflos
• Alexey Radul
• Allen Lavoie
• Ben Lee
• Billy Lamberta
• Brian Patton
• Christopher Suter
• Colin Carroll
• Dave Moore
• Du Phan
• Emily Fertig
• Faizan Muhammad
• George Necula
• George Tucker
• Grace Luo
• Ian Langmore
• Jacob Burnim
• Jake VanderPlas
• Jeremiah Liu
• Junpeng Lao
• Kaan
• Luke Wood
• Max Jiang
• Mihai Maruseac
• Neil Girdhar
• Paul Chiang
• Pavel Izmailov
• Pavel Sountsov
• Peter Hawkins
• Rebecca Chen
• Richard Song
• Rif A. Saurous
• Ron Shapiro
• Roy Frostig
• Sharad Vikram
• Srinivas Vasudevan
• Tomohiro Endo
• Urs Köster
• William C Grisaitis
• Yilei Yang

Release notes

This is the 0.14.1 release of TensorFlow Probability. It is tested and stable against TensorFlow version 2.6.0 and JAX 0.2.21.

Change notes

[coming soon]

Huge thanks to all the contributors to this release!

• 8bitmp3
• adriencorenflos
• allenl
• axch
• bjp
• blamb
• csuter
• colcarroll
• davmre
• derifatives
• emilyaf
• europeanplaice
• Frightera
• fmuham
• gcluo
• GianluigiSilvestri
• gisilvs
• gjt
• grisaitis
• harahu
• jburnim
• langmore
• leben
• lukewood
• mihaimaruseac
• NeilGirdhar
• phandu
• phawkins
• rechen
• ronshapiro
• scottzhu
• sharadmv
• siege
• srvasude
• ursk
• vanderplas
• xingyousong
• yileiyang

Release notes

This is the 0.14 release of TensorFlow Probability. It is tested and stable against TensorFlow version 2.6.0 and JAX 0.2.20.

Change notes

Please see the release notes for TFP 0.14.1 at https://github.com/tensorflow/probability/releases/v0.14.1 .

Huge thanks to all the contributors to this release!

• 8bitmp3
• adriencorenflos
• allenl
• axch
• bjp
• blamb
• csuter
• colcarroll
• davmre
• derifatives
• emilyaf
• europeanplaice
• Frightera
• fmuham
• gcluo
• GianluigiSilvestri
• gisilvs
• gjt
• grisaitis
• harahu
• jburnim
• langmore
• leben
• lukewood
• mihaimaruseac
• NeilGirdhar
• phandu
• phawkins
• rechen
• ronshapiro
• scottzhu
• sharadmv
• siege
• srvasude
• ursk
• vanderplas
• xingyousong
• yileiyang

Release notes

This is the 0.13 release of TensorFlow Probability. It is tested and stable against TensorFlow version 2.5.0.

See the visual release notebook in colab.

Change notes

• Distributions

  • Adds tfd.BetaQuotient
  • Adds tfd.DeterminantalPointProcess
  • Adds tfd.ExponentiallyModifiedGaussian
  • Adds tfd.MatrixNormal and tfd.MatrixT
  • Adds tfd.NormalInverseGaussian
  • Adds tfd.SigmoidBeta
  • Adds tfp.experimental.distribute.Sharded
  • Adds tfd.BatchBroadcast
  • Adds tfd.Masked
  • Adds JAX support for tfd.Zipf
  • Adds Implicit Reparameterization Gradients to tfd.InverseGaussian.
  • Adds quantiles for tfd.{Chi2,ExpGamma,Gamma,GeneralizedNormal,InverseGamma}
  • Derive Distribution batch shapes automatically from parameter annotations.
  • Ensuring Exponential.cdf(x) is always 0 for x < 0.
  • VectorExponentialLinearOperator and VectorExponentialDiag distributions now return variance, covariance, and standard deviation of the correct shape.
  • Bates distribution now returns mean of the correct shape.
  • GeneralizedPareto now returns variance of the correct shape.
  • Deterministic distribution now returns mean, mode, and variance of the correct shape.
  • Ensure that JointDistributionPinned's support bijectors respect autobatching.
  • Now systematically testing log_probs of most distributions for numerical accuracy.
  • InverseGaussian no longer emits negative samples for large loc / concentration
  • GammaGamma, GeneralizedExtremeValue, LogLogistic, LogNormal, ProbitBernoulli should no longer compute nan log_probs on their own samples. VonMisesFisher, Pareto, and GeneralizedExtremeValue should no longer emit samples numerically outside their support.
  • Improve numerical stability of tfd.ContinuousBernoulli and deprecate lims parameter.

• Bijectors

  • Add bijectors to mimic tf.nest.flatten (tfb.tree_flatten) and tf.nest.pack_sequence_as (tfb.pack_sequence_as).
  • Adds tfp.experimental.bijectors.Sharded
  • Remove deprecated tfb.ScaleTrilL. Use tfb.FillScaleTriL instead.
  • Adds cls.parameter_properties() annotations for Bijectors.
  • Extend range tfb.Power to all reals for odd integer powers.
  • Infer the log-deg-jacobian of scalar bijectors using autodiff, if not otherwise specified.

• MCMC

  • MCMC diagnostics support arbitrary structures of states, not just lists.
  • remc_thermodynamic_integrals added to tfp.experimental.mcmc
  • Adds tfp.experimental.mcmc.windowed_adaptive_hmc
  • Adds an experimental API for initializing a Markov chain from a near-zero uniform distribution in unconstrained space. tfp.experimental.mcmc.init_near_unconstrained_zero
  • Adds an experimental utility for retrying Markov Chain initialization until an acceptable point is found. tfp.experimental.mcmc.retry_init
  • Shuffling experimental streaming MCMC API to slot into tfp.mcmc with a minimum of disruption.
  • Adds ThinningKernel to experimental.mcmc.
  • Adds experimental.mcmc.run_kernel driver as a candidate streaming-based replacement to mcmc.sample_chain

• VI

  • Adds build_split_flow_surrogate_posterior to tfp.experimental.vi to build structured VI surrogate posteriors from normalizing flows.
  • Adds build_affine_surrogate_posterior to tfp.experimental.vi for construction of ADVI surrogate posteriors from an event shape.
  • Adds build_affine_surrogate_posterior_from_base_distribution to tfp.experimental.vi to enable construction of ADVI surrogate posteriors with correlation structures induced by affine transformations.

• MAP/MLE

  • Added convenience method tfp.experimental.util.make_trainable(cls) to create trainable instances of distributions and bijectors.

• Math/linalg

  • Add trapezoidal rule to tfp.math.
  • Add tfp.math.log_bessel_kve.
  • Add no_pivot_ldl to experimental.linalg.
  • Add marginal_fn argument to GaussianProcess (see no_pivot_ldl).
  • Added tfp.math.atan_difference(x, y)
  • Add tfp.math.erfcx, tfp.math.logerfc and tfp.math.logerfcx
  • Add tfp.math.dawsn for Dawson's Integral.
  • Add tfp.math.igammaincinv, tfp.math.igammacinv.
  • Add tfp.math.sqrt1pm1.
  • Add LogitNormal.stddev_approx and LogitNormal.variance_approx
  • Add tfp.math.owens_t for the Owen's T function.
  • Add bracket_root method to automatically initialize bounds for a root search.
  • Add Chandrupatla's method for finding roots of scalar functions.

• Stats

  • tfp.stats.windowed_mean efficiently computes windowed means.
  • tfp.stats.windowed_variance efficiently and accurately computes windowed variances.
  • tfp.stats.cumulative_variance efficiently and accurately computes cumulative variances.
  • RunningCovariance and friends can now be initialized from an example Tensor, not just from explicit shape and dtype.
  • Cleaner API for RunningCentralMoments, RunningMean, RunningPotentialScaleReduction.

• STS

  • Speed up STS forecasting and decomposition using internal tf.function wrapping.
  • Add option to speed up filtering in LinearGaussianSSM when only the final step's results are required.
  • Variational Inference with Multipart Bijectors: example notebook with the Radon model.
  • Add experimental support for transforming any distribution into a preconditioning bijector.

• Other

  • Distributed inference example notebook
  • sanitize_seed is now available in the tfp.random namespace.
  • Add tfp.random.spherical_uniform.

Huge thanks to all the contributors to this release!

• Abhinav Upadhyay
• axch
• Brian Patton
• Chris Jewell
• Christopher Suter
• colcarroll
• Dave Moore
• ebrevdo
• Emily Fertig
• Harald Husum
• Ivan Ukhov
• jballe
• jburnim
• Jeff Pollock
• Jensun Ravichandran
• JulianWgs
• junpenglao
• jvdillon
• j-wilson
• kateslin
• Kristian Hartikainen
• ksachdeva
• langmore
• leben
• mattjj
• Nicola De Cao
• Pavel Sountsov
• paweller
• phawkins
• Prasanth Shyamsundar
• Rene Jean Corneille
• Samuel Marks
• scottzhu
• sharadmv
• siege
• Simon Dirmeier
• Srinivas Vasudevan
• Thomas Markovich
• ursk
• Uzair
• vanderplas
• yileiyang
• ZeldaMariet
• Zichun Ye

This is the RC0 release candidate of the TensorFlow Probability 0.13 release.

It is tested against TensorFlow 2.5.0.

This is the 0.12.2 release of TensorFlow Probability, a patch release to cap the JAX dependency to a compatible version. It is tested and stable against TensorFlow version 2.4.0.

For detailed change notes, please see the 0.12.1 release at https://github.com/tensorflow/probability/releases/tag/v0.12.1 .

Release notes

This is the 0.12.1 release of TensorFlow Probability. It is tested and stable against TensorFlow version 2.4.0.

Change notes

NOTE: Links point to examples in the TFP 0.12.1 release Colab.

Bijectors:

• Add implementation of GLOW at tfp.bijectors.Glow.
• Add RayleighCDF bijector.
• Add Ascending bijector and deprecate Ordered.
• Add optional low parameter to the Softplus bijector.
• Enable ScaleMatvecLinearOperator bijector to wrap blockwise LinearOperators to form a multipart bijectors.
• Allow passing kwargs to Blockwise.
• Bijectors now share a global cache, keyed by the bijector parameters and the value being transformed.

Distributions:

• BREAKING: Remove deprecated HiddenMarkovModel.num_states property.
• BREAKING: Change the naming scheme of un-named variables in JointDistributions.
• BREAKING: Remove deprecated batch_shape and event_shape arguments of TransformedDistribution.
• Add Skellam distribution.
• JointDistributionCoroutine{AutoBatched} now uses namedtuples as the sample dtype.
• von-Mises Fisher distribution now works for dimensions > 5 and implements VonMisesFisher.entropy.
• Add ExpGamma and ExpInverseGamma distributions.
• JointDistribution*AutoBatched now support (reproducible) tensor seeds.
• Add KL(VonMisesFisher || SphericalUniform).
• Added Distribution.parameter_properties method.
• experimental_default_event_space_bijector now accepts additional arguments to pin some distribution parts.
• Add JointDistribution.experimental_pin and JointDistributionPinned.
• Add NegativeBinomial.experimental_from_mean_dispersion method.
• Add tfp.experimental.distribute, with DistributionStrategy-aware distributions that support cross-device likelihood computations.
• HiddenMarkovModel can now accept time varying observation distributions if time_varying_observation_distribution is set.
• Beta, Binomial, and NegativeBinomial CDF no longer returns nan outside the support.
• Remove the "dynamic graph" code path from the Mixture sampler. (Mixture now ignores the use_static_graph parameter.)
• Mixture now computes standard deviations more accurately and robustly.
• Fix incorrect nan samples generated by several distributions.
• Fix KL divergence between Categorical distributions when logits contain -inf.
• Implement Bernoulli.cdf.
• Add a log_rate parameter to tfd.Gamma.
• Add option for parallel filtering and sampling to LinearGaussianStateSpaceModel.

MCMC:

• Add tfp.experimental.mcmc.ProgressBarReducer.
• Update experimental.mcmc.sample_sequential_monte_carlo to use new MCMC stateless kernel API.
• Add an experimental streaming MCMC framework that supports computing statistics over a (batch of) Markov chain(s) without materializing the samples. Statistics supported (mostly on arbitrary functions of the model variables): mean, (co)variance, central moments of arbitrary rank, and the potential scale reduction factor (R-hat). Also support selectively tracing history of some but not all statistics or model variables. Add algorithms for running mean, variance, covariance, arbitrary higher central moments, and potential scale reduction factor (R-hat) totfp.experimental.stats.
• untempered_log_prob_fn added as init kwarg to ReplicaExchangeMC Kernel.
• Add experimental support for mass matrix preconditioning in Hamiltonian Monte Carlo.
• Add ability to temper part of the log prob in ReplicaExchangeMC.
• tfp.experimental.mcmc.{sample_fold,sample_chain} support warm restart.
• even_odd_swap exchange function added to replica_exchange_mc.
• Samples from ReplicaExchangeMC can now have a per-replica initial state.
• Add omitted n/(n-1) term to tfp.mcmc.potential_scale_reduction_factor.
• Add KernelBuilder and KernelOutputs to experimental.
• Allow tfp.mcmc.SimpleStepSizeAdaptation and DualAveragingStepSizeAdaptation to take a custom reduction function.
• Replace make_innermost_getter et al. with tfp.experimental.unnest utilities.

VI:

• Add automated construction of ASVI surrogate posteriors.

Math + Stats:

• Add tfp.math.bessel_ive, tfp.math.bessel_kve, tfp.math.log_bessel_ive.
• Add optional weights to tfp.stats.histogram.
• Add tfp.math.erfcinv.
• Add tfp.math.reduce_log_harmonic_mean_exp.

Other:

• Add tfp.math.psd_kernels.GeneralizedMaternKernel (generalizes MaternOneHalf, MaternThreeHalves and MaternFiveHalves).
• Add tfp.math.psd_kernels.Parabolic.
• Add tfp.experimental.unnest utilities for accessing nested attributes.
• Enable pytree flattening for TFP distributions in JAX
• More careful handling of nan and +-inf in {L-,}BFGS.
• Remove Edward2 from TFP. Edward2 is now in its own repo at https://github.com/google/edward2 .
• Support vector-valued offsets in sts.Sum.
• Make DeferredTensor actually defer computation under JAX/NumPy backends.

Huge thanks to all the contributors to this release!

• Adrian Buzea
• Alexey Radul
• Ben Lee
• Ben Poole
• Brian Patton
• Christopher Suter
• Colin Carroll
• Cyril Chimisov
• Dave Moore
• Du Phan
• Emily Fertig
• Eugene Brevdo
• Federico Tomasi
• François Chollet
• George Karpenkov
• Giovanni Palla
• Ian Langmore
• Jacob Burnim
• Jacob Valdez
• Jake VanderPlas
• Jason Zavaglia
• Jean-Baptiste Lespiau
• Jeff Pollock
• Joan Puigcerver
• Jonas Eschle
• Josh Darrieulat
• Joshua V. Dillon
• Junpeng Lao
• Kapil Sachdeva
• Kate Lin
• Kibeom Kim
• Luke Metz
• Mark Daoust
• Matteo Hessel
• Michal Brys
• Oren Bochman
• Padarn Wilson
• Pavel Sountsov
• Peter Hawkins
• Rif A. Saurous
• Ru Pei
• ST John
• Sharad Vikram
• Simeon Carstens
• Srinivas Vasudevan
• Tom O'Malley
• Tomer Kaftan
• Urs Köster
• Yash Katariya
• Yilei Yang

This is the 0.12.0 release of TensorFlow Probability. It is tested and stable against TensorFlow version 2.4.0.

For detailed change notes, please see the 0.12.1 release at https://github.com/tensorflow/probability/releases/tag/v0.12.1 .

This is RC4 of the TensorFlow Probability 0.12 release. It is tested against TensorFlow 2.4.0-rc4.

This is RC2 of the TensorFlow Probability 0.12 release. It is tested against TensorFlow 2.4.0-rc2.

This is RC1 of the TensorFlow Probability 0.12 release. It is tested against TensorFlow 2.4.0-rc1.

This is RC0 of the TensorFlow Probability 0.12 release. It is tested against TensorFlow 2.4.0-rc0.

This is a patch release for compatibility with CloudPickle >= 1.3. It is tested and stable against TensorFlow version 2.3.0.

Release notes

This is the 0.11 release of TensorFlow Probability. It is tested and stable against TensorFlow version 2.3.0.

Change notes

Links point to examples in the TFP 0.11.0 release Colab.

• Distributions

  • Support automatic vectorization in JointDistribution*AutoBatched instances.
  • Reproducible sampling, even in Eager.
  • Add Weibull distribution.
  • Add TruncatedCauchy distribution.
  • Add SphericalUniform distribution.
  • Add PowerSpherical distribution.
  • Add LogLogistic distribution.
  • Add Bates distribution.
  • Add GeneralizedNormal distribution.
  • Add JohnsonSU distribution.
  • Add ContinuousBernoulli distribution.
  • Simplify MultivariateNormalDiagPlusLowRank and make it tape-safer; remove deprecation.
  • Added KL(PowerSpherical || VonMisesFisher)
  • Adds KL(PowerSpherical || UniformSpherical), PowerSpherical.entropy and SphericalUniform.entropy
  • Fix gradient for Gamma samples with respect to rate parameter.
  • Increase accuracy of default Distribution.{log_}survival_function if log_cdf is implemented but cdf is not.
  • More accurate log_probs and entropies across many Distributions that were subtracting lgammas under the hood.
  • Fix Multinomial log_prob when classes have zero probability.
  • Improve performance of Multinomial sampler when total_count is high.
  • More accurate Binomial sampling and log_prob for large counts and small probabilities.
  • Binomial will no longer emit samples below 0 or above total_count.
  • Add nan handling for Bates log_prob and cdf.
  • Allow named arguments in JointDistribution*.sample().

• Bijectors:

  • Add the Split bijector.
  • Add GompertzCDF and ShiftedGompertzCDF bijectors
  • Add Sinh bijector.
  • Scale bijector can take in log_scale parameter.
  • Blockwise now supports size changing bijectors.
  • Allow using conditioning inputs in AutoregressiveNetwork.
  • Move bijector caching logic to its own library.

• MCMC:

  • tfp.mcmc now supports stateless sampling. tfp.mcmc.sample_chain(..., seed=(1,2)) is expected to always return the same results (within a release), and is deterministic (provided the underlying kernel is deterministic).
  • Better static shape inference for Metropolis-Hastings kernels with partially-specified shapes.
  • TransformedTransitionKernel nests properly with itself and other wrapper kernels.
  • Pretty-printing MCMC kernel results.

• Structured time series:

  • Automatically constrain STS inference when weights have constrained support.

• Math:

  • Add tfp.math.bessel_iv_ratio for ratios of modified bessel functions of the first kind.
  • round_exponential_bump_function added to tfp.math.
  • Support dynamic num_steps and custom convergence_criteria in tfp.math.minimize.
  • Add tfp.math.log_cosh.
  • Define more accurate lbeta and log_gamma_difference.

• Jax/Numpy substrates:

  • TFP runs on JAX!
  • Expose MaskedAutogregressiveFlow to Numpy and JAX.

• Experimental:

  • Add experimental Sequential Monte Carlo sample driver.
  • Add experimental tools for estimating parameters of sequential models using iterated filtering.
  • Use Distributions as CompositeTensors.
  • Inference Gym: Add logistic regression.
  • Add support for convergence criteria in tfp.vi.fit_surrogate_posterior.

• Other:

  • Added tfp.random.split_seed for stateless sampling. Moved tfp.math.random_{rademacher,rayleigh} to tfp.random.{rademacher,rayleigh}.
  • Possibly breaking change: SeedStream seed argument may not be a Tensor.

Huge thanks to all the contributors to this release!

• Alexey Radul
• anatoly
• Anudhyan Boral
• Ben Lee
• Brian Patton
• Christopher Suter
• Colin Carroll
• Cristi Cobzarenco
• Dan Moldovan
• Dave Moore
• David Kao
• Emily Fertig
• erdembanak
• Eugene Brevdo
• Fearghus Robert Keeble
• Frank Dellaert
• Gabriel Loaiza
• Gregory Flamich
• Ian Langmore
• Iqrar Agalosi Nureyza
• Jacob Burnim
• jeffpollock9
• jekbradbury
• Jimmy Yao
• johannespitz
• Joshua V. Dillon
• Junpeng Lao
• Kate Lin
• Ken Franko
• luke199629
• Mark Daoust
• Markus Kaiser
• Martin Jul
• Matthew Feickert
• Maxim Polunin
• Nicolas
• npfp
• Pavel Sountsov
• Peng YU
• Rebecca Chen
• Rif A. Saurous
• Ru Pei
• Sayam753
• Sharad Vikram
• Srinivas Vasudevan
• summeryue
• Tom Charnock
• Tres Popp
• Wataru Hashimoto
• Yash Katariya
• Zichun Ye

This is RC1 of the TensorFlow Probability 0.11 release. It is tested against TensorFlow 2.3.0-rc2.

This is RC0 of the TensorFlow Probability 0.11 release. It is tested against TensorFlow 2.3.0-rc1.

This is a patch release to pin the CloudPickle version to 1.3 to address #991 . It is tested and stable against TensorFlow version 2.2.0.

Release notes

This is the 0.10 release of TensorFlow Probability. It is tested and stable against TensorFlow version 2.2.0.

Change notes

• Distributions

  • Beta-Binomial distribution.
  • Add new AutoBatched joint distribution variants that treat a joint sample as a single probabilistic event.
  • XLA-able Python TF Gamma sampler.
  • XLA-able binomial sampler. Replaces the existing sampler, which implements binomial using one-hot categoricals via multinomial, with a batched rejection sampler. The new sampler is 4-6 times slower for very small problems, but an unbounded amount faster on large problems, since it removes a linear dependency on total_count. Additionally, since the previous solver required memory proportional to total_count*num_samples, many problems which OOM'd before are now feasible.
  • Enable use of joint bijectors in TransformedDistribution.
  • Remove unused get_logits_and_probs from internal/distribution_util.
  • Batched rejection sampling utilities.
  • Update batched_rejection_sampler to use prefer_static.shape to handle possibly-dynamic shape.

• Bijectors

  • Add Lambert W transform bijectors.

• MCMC

  • EllipticalSliceSampler in tfp.experimental.mcmc
  • Add cross-chain ESS, following Vehtari et al. 2019.

• Optimizer

  • Add convergence criteria for optimizations.

• Stats

  • Add tfp.stats.expected_calibration_error_quantiles.

• Math

  • Add a 'special' module to tfp.math - a TF version of scipy.special.
  • Add scan_associative function, implementing parallel prefix scan of tensors with a user-provided binary operation.

• Breaking change: Removed a number of functions, methods, and classes that were deprecated in TensorFlow Probability 0.9.0 or earlier.

  • Removed deprecated tfb.Weibull -- use tfb.WeibullCDF.
  • Remove VectorLaplaceLinearOperator
  • Remove deprecated method tfp.sts.build_factored_variational_loss.
  • Remove deprecated tfb.Kumaraswamy -- use tfb.Invert(tfb.KumaraswamyCDF).
  • Remove deprecated tfd.VectorSinhArcsinhDiag, tfd.VectorLaplaceDiag.
  • Remove deprecated tfb.Gumbel -- use tfb.GumbelCDF.

• Other

  • Python 3.8 compatibility.
  • TensorFlow now requires gast version 0.3.2 and is no longer compatible with 0.2.2.
  • Moving TF Session C++ to Python code and functionality from swig to pybind11.
  • Update TFP examples to Python 3.

Huge thanks to all the contributors to this release!

• Alexander Ivanov
• Alexey Radul
• Amanda
• Amelio Vazquez-Reina
• Amit Patankar
• Anudhyan Boral
• Artem Belevich
• Brian Patton
• Christopher Suter
• Colin Carroll
• Dan Moldovan
• Dave Moore
• Demetri Pananos
• Dmitrii Kochkov
• Emily Fertig
• gameshamilton
• Georg M. Goerg
• Ian Langmore
• Jacob Burnim
• jeffpollock9
• Joshua V. Dillon
• Junpeng Lao
• kovak1
• Kristian Hartikainen
• Liam
• Martin Jul
• Matt Hoffman
• nbro
• Olli Huotari
• Pavel Sountsov
• Pyrsos
• Rif A. Saurous
• Rushabh Vasani
• Sayam753
• Sharad Vikram
• Spyros
• Srinivas Vasudevan
• Taylor Robie
• Xiaojing Wang
• Zichun Ye

This is RC1 of the TensorFlow Probability 0.10 release. It is tested against TensorFlow 2.2.0-rc4.

This is the RC0 release candidate of the Tensorflow Probability 0.10 release. It is tested against Tensorflow 2.2.0-rc3.

Release notes

This is the 0.9 release of TensorFlow Probability. It is tested and stable against TensorFlow version 2.1.0.

NOTE: The 0.9 releases of TensorFlow Probability will be the last to support Python 2. Future versions of TensorFlow Probability will require Python 3.5 or later.

Change notes

• Distributions

  • Add Pixel CNN++ distribution.
  • Breaking change: Remove deprecated behavior of Poisson.rate and Poisson.log_rate.
  • Breaking change: Remove deprecated behavior of logits, probs properties.
  • Add _default_event_space_bijector to distributions.
  • Add validation that samples are within the support of the distribution.
  • Support positional and keyword args to JointDistribution.prob and JointDistribution.log_prob.
  • Support OrderedDict dtype in JointDistributionNamed.
  • tfd.BatchReshape is tape-safe
  • More accurate survival function and CDF for the generalized Pareto distribution.
  • Added Plackett-Luce distribution over permutations.
  • Fix long-standing bug with cdf, survival_function, and quantile for TransformedDistributions having decreasing bijectors.
  • Export the DoubleMaxwell distribution.
  • Add method for analytic Bayesian linear regression with LinearOperators.

• Bijectors

  • Breaking change: Scalar bijectors must implement _is_increasing if using cdf/survival_function/quantile on TransformedDistribution. This supports resolution of a long-standing bug, e.g. tfb.Scale(scale=-1.)(tfd.HalfNormal(0,1)).cdf was incorrect.
  • Deprecate tfb.masked_autoregressive_default_template.
  • Fixed inverse numerical stability bug in tfb.Softfloor
  • Tape-safe Reshape bijector.

• MCMC

  • Optimize tfp.mcmc.ReplicaExchangeMonteCarlo by replacing TF control flow and
  • ReplicaExchangeMC now can trace exchange proposals/acceptances.
  • Correct implementation of log_accept_ratio in NUTS
  • Return non-cumulated leapfrogs_taken in nuts kernel_result.
  • Make unrolled NUTS reproducible.
  • Bug fix of Generalized U-turn in NUTS.
  • Reduce NUTS test flakiness.
  • Fix convergence test for NUTS.
  • Switch back to original U turn criteria in Hoffman & Gelman 2014.
  • Make autobatched NUTS reproducible.

• STS

  • Update example "Structural Time Series Modeling Case Studies" to TF2.0 API.
  • Add fast path for sampling STS LocalLevel models.
  • Support posterior sampling in linear Gaussian state space models.
  • Add a fast path for Kalman smoothing with scalar latents.
  • Add option to disallow drift in STS Seasonal models.

• Breaking change: Removed a number of functions, methods, and classes that were deprecated in TensorFlow Probability 0.8.0 or earlier.

  • Remove deprecated trainable_distributions_lib.
  • Remove deprecated property Dirichlet.total_concentration.
  • Remove deprecated tfb.AutoregressiveLayer -- use tfb.AutoregressiveNetwork.
  • Remove deprecated tfp.distributions.* methods.
  • Remove deprecated tfp.distributions.moving_mean_variance.
  • Remove two deprecated tfp.vi functions.
  • Remove deprecated tfp.distributions.SeedStream -- use tfp.util.SeedStream.
  • Remove deprecated properties of tfd.Categorical.

• Other

  • Add make_rank_polymorphic utility, which lifts a callable to a vectorized callable.
  • Dormand-Prince solver supports nested structures. Implemented adjoint sensitivity method for Dormand-Prince solver gradients.
  • Run Travis tests against latest tf-estimator-nightly.
  • Supporting gast 0.3 +
  • Add tfp.vi.build_factored_surrogate_posterior utility for automatic black-box variational inference.

Huge thanks to all the contributors to this release!

• Aditya Grover
• Alexey Radul
• Anudhyan Boral
• Arthur Lui
• Billy Lamberta
• Brian Patton
• Christopher Suter
• Colemak
• Dan Moldovan
• Dave Moore
• Dmitrii Kochkov
• Edward Loper
• Emily Fertig
• Ian Langmore
• Jacob Burnim
• Joshua V. Dillon
• Junpeng Lao
• Katherine Wu
• Kibeom Kim
• Kristian Hartikainen
• Mark Daoust
• Pavel Sountsov
• Peter Hawkins
• refraction-ray
• RJ Skerry-Ryan
• Sanket Kamthe
• Sergei Lebedev
• Sharad Vikram
• Srinivas Vasudevan
• Yanhua Sun
• Yash Katariya
• Zachary Nado

Release notes

This is the 0.8 release of TensorFlow Probability. It is tested and stable against TensorFlow version 2.0.0 and 1.15.0rc1.

Change notes

• GPU-friendly "unrolled" NUTS: tfp.mcmc.NoUTurnSampler

  • Open-source the unrolled implementation of the No U-Turn Sampler.
  • Switch back to original U turn criteria in Hoffman & Gelman 2014.
  • Bug fix in Unrolled NUTS to make sure it does not lose shape for event_shape=1.
  • Bug fix of U turn check in Unrolled NUTS at the tree extension.
  • Refactor U turn check in Unrolled NUTS.
  • Fix dynamic shape bug in Unrolled NUTS.
  • Move NUTS unrolled into mcmc, with additional clean up.
  • Make sure the unrolled NUTS sampler handle scalar target_log_probs correctly.
  • Change implementation of check U turn to using a tf.while_loop in unrolled NUTS.
  • Implement multinomial sampling across tree (instead of Slice sampling) in unrolled NUTS.
  • Expose additional diagnostics in previous_kernel_results in unrolled NUTS so that it works with *_step_size_adaptation.

• MCMC

  • Modify the shape handling in DualAveragingStepSizeAdaptation so that it works with non-scalar event_shape.
  • support structured samples in tfp.monte_carlo.expectation.
  • Minor fix for docstring example in leapfrog_integrator

• VI

  • Add utilities for fitting variational distributions.
  • Improve Csiszar divergence support for joint variational distributions.
  • ensure that joint distributions are correctly recognized as reparameterizable by monte_carlo_csiszar_f_divergence.
  • Rename monte_carlo_csiszar_f_divergence to monte_carlo_variational_loss.
  • Refactor tfp.vi.csiszar_vimco_helper to expose useful leave-one-out statistical tools.

• Distributions

  • Added tfp.distributions.GeneralizedPareto
  • Multinomial and DirichletMultinomial samplers are now reproducible.
  • HMM samples are now reproducible.
  • Cleaning up unneeded conversion to tensor in quantile().
  • Added support for dynamic num_steps in HiddenMarkovModel
  • Added implementation of quantile() for exponential distributions.
  • Fix entropy of Categorical distribution when logits contains -inf.
  • Annotate float-valued Deterministic distributions as reparameterized.
  • Establish patterns which ensure that TFP objects are "GradientTape Safe."
  • "GradientTape-safe" distributions: FiniteDiscrete, VonMises, Binomial, Dirichlet, Multinomial, DirichletMultinomial, Categorical, Deterministic
  • Add tfp.util.DeferredTensor to delay Tensor operations on tf.Variables (also works for tf.Tensors).
  • Add probs_parameter, logits_parameter member functions to Categorical-like distributions. In the future users should use these new functions rather than probs/logits properties because the properties might be None if that's how the distribution was parameterized.

• Bijectors

  • Add log_scale parameter to AffineScalar bijector.
  • Added tfp.bijectors.RationalQuadraticSpline.
  • Add SoftFloor bijector. (Note: Known inverse bug WIP.)
  • Allow using an arbitrary bijector in RealNVP for the coupling.
  • Allow using an arbitrary bijector in MaskedAutoregressiveFlow for the coupling.

• Experimental auto-batching system: tfp.experimental.auto_batching

  • Open-source the program-counter-based auto-batching system.
  • Added tfp.experimental.auto_batching, an experimental system to recover batch parallelism across recursive function invocations.
  • Autobatched NUTS supports batching across consecutive trajectories.
  • Add support for field references to autobatching.
  • Increase the amount of Python syntax that "just works" in autobatched functions.
  • pop-push fusion optimization in the autobatching system (also recently did tail-call optimization but forgot to add a relnote).
  • Open-source the auto-batched implementation of the No U-Turn Sampler.

• STS

  • Support TF2/Eager-mode fitting of STS models, and deprecate build_factored_variational_loss.
  • Use dual averaging step size adaptation for STS HMC fitting.
  • Add support for imputing missing values in structural time series models.
  • Standardize parameter scales during STS inference.

• Layers

  • Add WeightNorm layer wrapper.
  • Fix gradients flowing through variables in the old style variational layers.
  • tf.keras.model.save_model and model.save now defaults to saving a TensorFlow SavedModel.

• Stats/Math

  • Add calibration metrics to tfp.stats.
  • Add output_gradients argument to value_and_gradient.
  • Add Geyer initial positive sequence truncation criterion to tfp.mcmc.effective_sample_size.
  • Resolve shape inconsistencies in PSDKernels API.
  • Support dynamic-shaped results in tfp.math.minimize.
  • ODE: Implement the Adjoint Method for gradients with respect to the initial state.

Huge thanks to all the contributors to this release!

• Alexey Radul
• Anudhyan Boral
• Arthur Lui
• Brian Patton
• Christopher Suter
• Colin Carroll
• Dan Moldovan
• Dave Moore
• Edward Loper
• Emily Fertig
• Gaurav Jain
• Ian Langmore
• Igor Ganichev
• Jacob Burnim
• Jeff Pollock
• Joshua V. Dillon
• Junpeng Lao
• Katherine Wu
• Mark Daoust
• Matthieu Coquet
• Parsiad Azimzadeh
• Pavel Sountsov
• Pavithra Vijay
• PJ Trainor
• prabhu prakash kagitha
• prakashkagitha
• Reed Wanderman-Milne
• refraction-ray
• Rif A. Saurous
• RJ Skerry-Ryan
• Saurabh Saxena
• Sharad Vikram
• Sigrid Keydana
• skeydan
• Srinivas Vasudevan
• Yash Katariya
• Zachary Nado

This is the RC0 release candidate of the TensorFlow Probability 0.8 release.

It is tested against TensorFlow 2.0.0-rc0

This is the 0.7.0-rc0 release of TensorFlow Probability. It is tested and stable against TensorFlow version 1.14-rc0 and 2.0.0-alpha

Release notes

This is the 0.6 release of TensorFlow Probability. It is tested and stable against TensorFlow version 1.13.1.

Change notes

• Adds tfp.positive_semidefinite_kernels.RationalQuadratic
• Support float64 in tfpl.MultivariateNormalTriL.
• Add IndependentLogistic and IndependentPoisson distribution layers.
• Add make_value_setter interceptor to set values of Edward2 random variables.
• Implementation of Kalman Smoother, as a member function of LinearGaussianStateSpaceModel.
• Bijector caching is enabled only in one direction when executing in eager mode. May cause some performance regression in eager mode if repeatedly computing forward(x) or inverse(y) with the same x or y value.
• Handle rank-0/empty event_shape in tfpl.Independent{Bernoulli,Normal}.
• Run additional tests in eager mode.
• quantiles(x, n, ...) added to tfp.stats.
• Makes tensorflow_probability compatible with Tensorflow 2.0 TensorShape indexing.
• Use scipy.special functions when testing KL divergence for Chi, Chi2.
• Add methods to create forecasts from STS models.
• Add a MixtureSameFamily distribution layer.
• Add Chi distribution.
• Fix doc typo tfp.Distribution -> tfd.Distribution.
• Add Gumbel-Gumbel KL divergence.
• Add HalfNormal-HalfNormal KL divergence.
• Add Chi2-Chi2 KL divergence unit tests.
• Add Exponential-Exponential KL divergence unit tests.
• Add sampling test for Normal-Normal KL divergence.
• Add an IndependentNormal distribution layer.
• Added posterior_marginals to HiddenMarkovModel
• Add Pareto-Pareto KL divergence.
• Add LinearRegression component for structural time series models.
• Add dataset ops to the graph (or create kernels in Eager execution) during the python Dataset object creation instead doing it during Iterator creation time.
• Text messages HMC benchmark.
• Add example notebook encoding a switching Poisson process as an HMM for multiple changepoint detection.
• Require num_adaptation_steps argument to make_simple_step_size_update_policy.
• s/eight_hmc_schools/eight_schools_hmc/ in printed benchmark string.
• Add tfp.layers.DistributionLambda to enable plumbing tfd.Distribution instances through Keras models.
• Adding tfp.math.batch_interp_regular_1d_grid.
• Update description of fill_triangular to include an in-depth example.
• Enable bijector/distribution composition, eg, tfb.Exp(tfd.Normal(0,1)).
• linear and midpoint interpolation added to tfp.stats.percentile.
• Make distributions include only the bijectors they use.
• tfp.math.interp_regular_1d_grid added
• tfp.stats.correlation added (Pearson correlation).
• Update list of edward2 RVs to include recently added Distributions.
• Density of continuous Uniform distribution includes the upper endpoint.
• Add support for batched inputs in tfp.glm.fit_sparse.
• interp_regular_1d_grid added to tfp.math.
• Added HiddenMarkovModel distribution.
• Add Student's T Process.
• Optimize LinearGaussianStateSpaceModel by avoiding matrix ops when the observations are statically known to be scalar.
• stddev, cholesky added to tfp.stats.
• Add methods to fit structual time series models to data with variational inference and HMC.
• Add Expm1 bijector (Y = Exp(X) - 1).
• New stats namespace. covariance and variance added to tfp.stats
• Make all available MCMC kernels compatible with TransformedTransitionKernel.

Huge thanks to all the contributors to this release!

• Adam Wood
• Alexey Radul
• Anudhyan Boral
• Ashish Saxena
• Billy Lamberta
• Brian Patton
• Christopher Suter
• Cyril Chimisov
• Dave Moore
• Eugene Zhulenev
• Griffin Tabor
• Ian Langmore
• Jacob Burnim
• Jakub Arnold
• Jiahao Yao
• Jihun
• Jiming Ye
• Joshua V. Dillon
• Juan A. Navarro Pérez
• Julius Kunze
• Julius Plenz
• Kristian Hartikainen
• Kyle Beauchamp
• Matej Rizman
• Pavel Sountsov
• Peter Roelants
• Rif A. Saurous
• Rohan Jain
• Roman Ring
• Rui Zhao
• Sergio Guadarrama
• Shuhei Iitsuka
• Shuming Hu
• Srinivas Vasudevan
• Tabor473
• ValentinMouret
• Youngwook Kim
• Yuki Nagae

This is the 0.6.0-rc1 release candidate of TensorFlow Probability. It is tested against TensorFlow 1.13.0-rc2.

This is the RC0 release candidate of the TensorFlow Probability 0.6 release.

It is tested against TensorFlow 1.13.0-rc0

Release Notes

This is the 0.5.0 release of TensorFlow Probability. It's tested and stable against TensorFlow 1.12.

Packaging Change

As of this release, we no longer package a separate GPU-specific build. Users can select the version of TensorFlow they wish to use (CPU or GPU), and TensorFlow Probability will work with both.

As a result, we no longer explicitly list a TensorFlow dependency in our package requirements (since we can't know which version the user will want). If TFP is installed with no TensorFlow package present, or with an unsupported TensorFlow version, we will issue an ImportError at time of import.

Distributions & Bijectors

• All Distributions have been relocated from tf.distributions to tfp.distributions (the ones in TF are deprecated and will be deleted in TF 2.0).
• Add Triangular distribution.
• Add Zipf distribution.
• Add NormalCDF Bijector.
• Add Multivariate Student's t-distribution.
• Add RationalQuadratic kernel.

Documentation & Examples

• Add example showing how to fit GLMM using Variational Inference.
• Introduce Gaussian process latent variable model colab.
• Introduce Gaussian process regression example colab
• Add notebook showcasing GLM algorithms and deriving some results about GLMs that those algorithms leverage.

Huge thanks to all the contributors to this release!

• Akshay Modi
• Alexey Radul
• Anudhyan Boral
• Ashish Saxena
• Ben Zinberg
• Billy Lamberta
• Brian Patton
• Christopher Suter
• Dave Moore
• Ian Langmore
• Joshua V. Dillon
• Kristian Hartikainen
• Malcolm Reynolds
• Pavel Sountsov
• Srinivas Vasudevan
• Xiaojing Wang
• Yifei Feng

This is the RC1 release candidate of the TensorFlow Probability 0.5 release.

It is tested against TensorFlow 1.12.0-rc2

This is the RC0 release candidate of the TensorFlow Probability 0.5 release.

It is tested against TensorFlow 1.12.0-rc2

Release Notes

This is the 0.4.0 release of TensorFlow Probability. It's tested and stable against TensorFlow 1.11.

Distributions & Bijectors

• All TF core Distributions now live in TFP (old ones are deprecated but still there)
• All TF core Bijectors now live in TFP (old ones are deprecated but still there)
• CDF support for Independent
• Seasonal effects and Sum components for Structural Time Series
• Add Half-Cauchy Distribution
• DCT (discrete cosine transform) Bijector
• Support dynamic shapes in BatchNormalization Bijector
• Add InverseGaussian Distribution
• Scaffolding for Bayesian Structural Time Series models
• Add VonMises Distribution
• Add Pareto distrubiton
• Implicit reparameterization for TruncatedNormal

Sampling & Inference

• Various improvements to BFGS and line search methods
• Add tape interceptor in Edward2
• GLM fitting with proximal gradient and L1/L2-regularization
• Support nested interceptors in Edward2

Documentation & Examples

• Update docs to point to tfp instead of tf/tf.contrib
• Credit card interest notebook
• Bayesian Neural Net (VGG, ResNet18) examples on CIFAR-10 data
• Disentangled Sequential Autoencoder example
• Eager-mode, single-chain No-U-Turn Sampler
• Grammar VAE example using Edward2
• Deep exponential family example using Edward2
• Latent Dirichlet Allocation example using Edward2
• Factorial Mixtures notebook

Huge thanks to all the contributors to this release!

• Asim Shankar
• Ben Zinberg
• Billy Lamberta
• Brian Patton
• Christopher Suter
• Copybara-Service
• cyrilchimisov
• Dave Moore
• davmre
• Dustin Tran
• Ian Langmore
• jiamingz
• Jiaming Zeng
• Jonathan J Hunt
• Joshua V. Dillon
• Keyon Vafa
• Mark Daoust
• Matthew McAteer
• mgorinova
• mhoffman
• Michael Figurnov
• Mike Dusenberry
• rif
• saxeas
• srvasude
• William D. Irons
• yunhao

This is the 0th release candidate of the 0.4.0 release of TensorFlow Probability.

It is tested against TensorFlow v1.11.0

Release Notes

This is the 0.3.0 release of TensorFlow Probability. It's tested and stable against TensorFlow 1.10.

Distributions & Bijectors

• Add the LKJ distribution on correlation matrices.
• Add GammaGamma distribution.
• Adds the VonMisesFisher distribution over points on the unit hypersphere.
• Add CholeskyToInvCholesky bijector.
• Added reparametrizable TruncatedNormal
• Add tfp.bijectors.Transpose.
• Add tanh bijection.
• Introduce GaussianProcessRegressionModel
• Introduce GaussianProcess distribution
• Gamma distribution and the derived distributions (Beta, Dirichlet, Student's t, inverse Gamma) are fully reparameterized.
• Add low and high as properties to quantized distribution.
• Collapse WishartCholesky and WishartFull into a single Wishart distribution that takes either a scale or a scale_cholesky argument.
• Add adjoint arg to tfp.bijectors.Affine.

Sampling & Inference

• Enable nested interceptors in Edward2.
• Provide interface for controlling the number of HMC iterations during which to adapt the step size.
• Added support for dynamic shapes in the slice sampler.
• Make HMC more efficient and usable for MCEM.
  • Allow stop_gradient to be applied as new state is built (thus enabling recycling kernel_results.accepted.target_log_prob).
    • Add hook for user defined adaptive step size code and provide default implementation.
• Added implementation of the Nelder Mead derivative free optimization method.
• Add tfp.math.random_rayleigh.

Documentation & Examples

• Add Edward2 README.md.
• Add migration guide from Edward to TFP.
• Add documentation matching tfp-0.2 release.
• Add colab example which compares fitting HLM's between TF distributions, Stan, and R. Colab was written in collaboration with safyan@.
• Added a preliminary version of a Probabilistic PCA Edward 2 example, and changed the BUILD file accordingly.
• Latent Dirichlet Allocation for 20 newsgroups dataset.
• A detailed case study in using TensorFlow Probability for estimating a covariance matrix.

Huge thanks to all the contributors to this release!

• Akshay Agrawal
• Billy Lamberta
• Brian Patton
• Christopher Suter
• cyrilchimisov
• davmre
• Dustin Tran
• Ian Langmore
• jjhunt
• Joshua V. Dillon
• Kousuke Ariga
• Michael Figurnov
• Michele Colombo
• rif
• saxeas
• srvasude
• William D. Irons
• Yuan Huang

This is the rc1 release. We never actually built rc1, since we needed to cherrypick a few more things.

This release is tested against TensorFlow v1.10.0

This is the 0.2 release of TensorFlow Probability, our first versioned release.

It is tested against TensorFlow 1.9.0.

This is release candidate rc0, of our 0.2 release of TensorFlow Probability.

It is tested against TensorFlow 1.9.0.

Release 0.1.0-rc1

This is release candidate rc1, of our first versioned release of TensorFlow Probability.

It is tested against TensorFlow 1.9.0-rc1.

Release 0.1.0-rc0

This is the 0th release candidate our first versioned release of TensorFlow Probability.