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Commits on Aug 19, 2020

• Numerical tweak to Dirichlet sampling.

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Commits on Aug 18, 2020

• Numerical tweak for Beta sampling.

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• Numerical tweak for inverse gamma sampling.

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• Numerical tweak to DirichletMultinomial sampling.

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• Numerical tweak for GammaGamma sampling.

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• Use log-space sampler in BetaBinomial.

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• Numerical tweak to Wishart sampling.

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Commits on Aug 17, 2020

• Modifies gamma sampling to support log-space sampling even when using the faster CPU stateless_gamma.

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• Mark LGCP test for jax no-oss-ci.

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Commits on Aug 14, 2020

• Use an explicit list instead of a tag to accumulate targets for the test_suite.

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Commits on Aug 13, 2020

• Add log_space and log_rate arguments to random_gamma_rejection. This will be immediately useful to BetaBinomial and DirichletMultinomial.

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Commits on Aug 10, 2020

• Remove usages of samplers.gamma in favor of the implementation-selected rejection samplers in distributions/gamma.py. This yields a variety of gamma-dependent samplers that are now XLA/TPU-friendly.

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Commits on Aug 5, 2020

• Enable omnistaging in TFP on JAX test cases.

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• Update TFP distributions in prep for omnistaging.

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Commits on Aug 3, 2020

• Update TFP bijectors in prep for omnistaging.

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• Update TFP MCMC in prep for omnistaging.

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• Update TFP internals to support JAX omnistaging.

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• Update TFP optimizers in prep for omnistaging.

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• Update TFP math in prep for omnistaging.

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Commits on Jul 31, 2020

• Update tf2jax in prep for omnistaging.

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• Update TFP stats in prep for omnistaging.

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• Update inference gym in prep for omnistaging.

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Commits on Jul 30, 2020

• Some minor tweaks and fixes to np/jax backends.

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• TruncatedNormal sampling in XLA was broken by the change to stateless.

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Commits on Jul 28, 2020

• Add TFP 0.11.0 release notes notebook.

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• TruncatedNormal is now vectorized_map friendly.

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• Enable autovectorization testing for the Gamma distribution.

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Commits on Jul 24, 2020

• Custom JVP for von-Mises samples.

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• Resolving Gamma and GeneralizedNormal JVP failures.

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Commits on Jul 23, 2020

• TruncatedNormal modified to use tf.random.stateless_parameterized_truncated_normal, and jax/scipy implementations. This eliminates the need for custom gradients, since both TF and JAX have derivati…

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• Filter out broken sub-distributions from meta-distribution tests.

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• Better strategy for reshape argument of BatchReshape bijector.

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• Custom JVP for log_cosh yields working JVPs for JohnsonSU distribution.

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• Custom derivatives for gamma samples would enable JAX JVPs, but for jax-ml/jax#3822

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Commits on Jul 22, 2020

• Protect against missing wrapt in case we are in a no-TensorFlow scenario.

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