v0.25.0

Release notes

This is the 0.25 release of TensorFlow Probability. It is
tested and stable against TensorFlow version 2.18 and JAX 0.4.35.

NOTE: In TensorFlow 2.16+, tf.keras (and tf.initializers, tf.losses, and tf.optimizers) refers to Keras 3. TensorFlow Probability is not compatible with Keras 3 -- instead TFP is continuing to use Keras 2, which is now packaged as tf-keras and tf-keras-nightly and is imported as tf_keras. When using TensorFlow Probability with TensorFlow, you must explicitly install Keras 2 along with TensorFlow (or install tensorflow-probability[tf] or tfp-nightly[tf] to automatically install these dependencies.)

Change notes

• Add mean + variance to tfd.Categorical.

Huge thanks to all the contributors to this release!

• bjp
• Chris Jewell
• Christopher Suter
• colcarroll
• emilyaf
• feyu
• jburnim
• leben
• lukes
• mrry
• phawkins
• siege
• Srinivas Vasudevan
• swijaya
• thomaswc
• ursk
• vanderplas

TensorFlow Probability 0.24.0

Release notes

This is the 0.24.0 release of TensorFlow Probability. It is tested and stable against TensorFlow 2.16.1 and JAX 0.4.25 .

NOTE: In TensorFlow 2.16+, tf.keras (and tf.initializers, tf.losses, and tf.optimizers) refers to Keras 3. TensorFlow Probability is not compatible with Keras 3 -- instead TFP is continuing to use Keras 2, which is now packaged as tf-keras and tf-keras-nightly and is imported as tf_keras. When using TensorFlow Probability with TensorFlow, you must explicitly install Keras 2 along with TensorFlow (or install tensorflow-probability[tf] or tfp-nightly[tf] to automatically install these dependencies.)

Change notes

• TensorFlow Probability now supports Python 3.12.

  • But note that many parts of tfp.layers and tfp.experimental.nn will raise errors because of a TensorFlow + wrapt bug (see tensorflow/tensorflow#60687 ), which can be worked around by setting the environment variable WRAPT_DISABLE_EXTENSIONS=true.

• Added an experimental implementation of Chopin, Jacob, Papaspiliopoulos, "SMC^2: an efficient algorithm for sequential analysis of state-space models", Journal of the Royal Statistical Society Series B: Statistical Methodology 75.3 (2013). See https://github.com/tensorflow/probability/blob/v0.24.0/tensorflow_probability/python/experimental/mcmc/particle_filter.py#L766 .

• Added tfp.experimental.fastgp, a library for approximately training and evaluating Gaussian Processes in sub-O(n^3) time.
  See https://github.com/tensorflow/probability/tree/r0.24/tensorflow_probability/python/experimental/fastgp .

Huge thanks to all the contributors to this release!

• Alessandro Slamitz
• Christopher Suter
• Colin Carroll
• Emily Fertig
• Gareth Williams
• Jacob Burnim
• Jake VanderPlas
• Matthew Feickert
• Pavel Sountsov
• Richard Levasseur
• Srinivas Vasudevan
• Thomas Colthurst
• Urs Köster

TensorFlow Probability 0.23

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

TensorFlow Probability 0.22.1

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

TensorFlow Probability 0.22.0

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

TensorFlow Probability 0.21.0

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

TensorFlow Probability 0.20.0

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

TensorFlow Probability 0.19.0

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

TensorFlow Probability 0.18.0

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]

TensorFlow Probability 0.17.0

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