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