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TensorFlow Probability 0.15.0

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@csuter csuter released this 18 Nov 15:49
v0.15.0
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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
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