Bump tensorflow-probability from 0.11.1 to 0.15.0

[dependabot]

Bumps tensorflow-probability from 0.11.1 to 0.15.0.

Release notes

Sourced from tensorflow-probability's releases.

TensorFlow Probability 0.15.0

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.

... (truncated)

Commits

• f377715 Merge pull request #1466 from emilyfertig/r0.15
• 47f35da Remove 'dev' suffix from version.
• 02f31b5 Replace value in simple_step_size_adaptation_test to work around a test fai...
• 286af5b Revert "Change references of tf.internal.saved_model.StructureCoder to th...
• 76eaa7e Fix NaNs when running the spike-and-slab sampler under XLA.
• 8870950 Set Softplus.dtype when hinge_softness or low is specified.
• 2ad13b1 Simplify DeferredModule interface by combining base_class and args_fn int...
• 6e8ef0e Centralize generic loop utilities under internal/loop_util.
• 8944243 Support sparse regression with spike-and-slab priors in the STS Gibbs sampler.
• cd35db9 Use float64 instead of float32 in tfd.Beta and tfd.Binomial tests to work aro...
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[dependabot]

The following labels could not be found: type:dependencies.

[dependabot]

Superseded by #44.