TensorFlow Probability 0.11.0

Release notes

This is the 0.11 release of TensorFlow Probability. It is tested and stable against TensorFlow version 2.3.0.

Change notes

Links point to examples in the TFP 0.11.0 release Colab.

• Distributions

  • Support automatic vectorization in JointDistribution*AutoBatched instances.
  • Reproducible sampling, even in Eager.
  • Add Weibull distribution.
  • Add TruncatedCauchy distribution.
  • Add SphericalUniform distribution.
  • Add PowerSpherical distribution.
  • Add LogLogistic distribution.
  • Add Bates distribution.
  • Add GeneralizedNormal distribution.
  • Add JohnsonSU distribution.
  • Add ContinuousBernoulli distribution.
  • Simplify MultivariateNormalDiagPlusLowRank and make it tape-safer; remove deprecation.
  • Added KL(PowerSpherical || VonMisesFisher)
  • Adds KL(PowerSpherical || UniformSpherical), PowerSpherical.entropy and SphericalUniform.entropy
  • Fix gradient for Gamma samples with respect to rate parameter.
  • Increase accuracy of default Distribution.{log_}survival_function if log_cdf is implemented but cdf is not.
  • More accurate log_probs and entropies across many Distributions that were subtracting lgammas under the hood.
  • Fix Multinomial log_prob when classes have zero probability.
  • Improve performance of Multinomial sampler when total_count is high.
  • More accurate Binomial sampling and log_prob for large counts and small probabilities.
  • Binomial will no longer emit samples below 0 or above total_count.
  • Add nan handling for Bates log_prob and cdf.
  • Allow named arguments in JointDistribution*.sample().

• Bijectors:

  • Add the Split bijector.
  • Add GompertzCDF and ShiftedGompertzCDF bijectors
  • Add Sinh bijector.
  • Scale bijector can take in log_scale parameter.
  • Blockwise now supports size changing bijectors.
  • Allow using conditioning inputs in AutoregressiveNetwork.
  • Move bijector caching logic to its own library.

• MCMC:

  • tfp.mcmc now supports stateless sampling. tfp.mcmc.sample_chain(..., seed=(1,2)) is expected to always return the same results (within a release), and is deterministic (provided the underlying kernel is deterministic).
  • Better static shape inference for Metropolis-Hastings kernels with partially-specified shapes.
  • TransformedTransitionKernel nests properly with itself and other wrapper kernels.
  • Pretty-printing MCMC kernel results.

• Structured time series:

  • Automatically constrain STS inference when weights have constrained support.

• Math:

  • Add tfp.math.bessel_iv_ratio for ratios of modified bessel functions of the first kind.
  • round_exponential_bump_function added to tfp.math.
  • Support dynamic num_steps and custom convergence_criteria in tfp.math.minimize.
  • Add tfp.math.log_cosh.
  • Define more accurate lbeta and log_gamma_difference.

• Jax/Numpy substrates:

  • TFP runs on JAX!
  • Expose MaskedAutogregressiveFlow to Numpy and JAX.

• Experimental:

  • Add experimental Sequential Monte Carlo sample driver.
  • Add experimental tools for estimating parameters of sequential models using iterated filtering.
  • Use Distributions as CompositeTensors.
  • Inference Gym: Add logistic regression.
  • Add support for convergence criteria in tfp.vi.fit_surrogate_posterior.

• Other:

  • Added tfp.random.split_seed for stateless sampling. Moved tfp.math.random_{rademacher,rayleigh} to tfp.random.{rademacher,rayleigh}.
  • Possibly breaking change: SeedStream seed argument may not be a Tensor.

Huge thanks to all the contributors to this release!

• Alexey Radul
• anatoly
• Anudhyan Boral
• Ben Lee
• Brian Patton
• Christopher Suter
• Colin Carroll
• Cristi Cobzarenco
• Dan Moldovan
• Dave Moore
• David Kao
• Emily Fertig
• erdembanak
• Eugene Brevdo
• Fearghus Robert Keeble
• Frank Dellaert
• Gabriel Loaiza
• Gregory Flamich
• Ian Langmore
• Iqrar Agalosi Nureyza
• Jacob Burnim
• jeffpollock9
• jekbradbury
• Jimmy Yao
• johannespitz
• Joshua V. Dillon
• Junpeng Lao
• Kate Lin
• Ken Franko
• luke199629
• Mark Daoust
• Markus Kaiser
• Martin Jul
• Matthew Feickert
• Maxim Polunin
• Nicolas
• npfp
• Pavel Sountsov
• Peng YU
• Rebecca Chen
• Rif A. Saurous
• Ru Pei
• Sayam753
• Sharad Vikram
• Srinivas Vasudevan
• summeryue
• Tom Charnock
• Tres Popp
• Wataru Hashimoto
• Yash Katariya
• Zichun Ye