Release notes

This is the 0.13 release of TensorFlow Probability. It is
tested and stable against TensorFlow version 2.5.0.

See the visual release notebook in colab.

Change notes

• Distributions

  • Adds tfd.BetaQuotient
  • Adds tfd.DeterminantalPointProcess
  • Adds tfd.ExponentiallyModifiedGaussian
  • Adds tfd.MatrixNormal and tfd.MatrixT
  • Adds tfd.NormalInverseGaussian
  • Adds tfd.SigmoidBeta
  • Adds tfp.experimental.distribute.Sharded
  • Adds tfd.BatchBroadcast
  • Adds tfd.Masked
  • Adds JAX support for tfd.Zipf
  • Adds Implicit Reparameterization Gradients to tfd.InverseGaussian.
  • Adds quantiles for tfd.{Chi2,ExpGamma,Gamma,GeneralizedNormal,InverseGamma}
  • Derive Distribution batch shapes automatically from parameter annotations.
  • Ensuring Exponential.cdf(x) is always 0 for x < 0.
  • VectorExponentialLinearOperator and VectorExponentialDiag distributions now return variance, covariance, and standard deviation of the correct shape.
  • Bates distribution now returns mean of the correct shape.
  • GeneralizedPareto now returns variance of the correct shape.
  • Deterministic distribution now returns mean, mode, and variance of the correct shape.
  • Ensure that JointDistributionPinned's support bijectors respect autobatching.
  • Now systematically testing log_probs of most distributions for numerical accuracy.
  • InverseGaussian no longer emits negative samples for large loc / concentration
  • GammaGamma, GeneralizedExtremeValue, LogLogistic, LogNormal, ProbitBernoulli should no longer compute nan log_probs on their own samples. VonMisesFisher, Pareto, and GeneralizedExtremeValue should no longer emit samples numerically outside their support.
  • Improve numerical stability of tfd.ContinuousBernoulli and deprecate lims parameter.

• Bijectors

  • Add bijectors to mimic tf.nest.flatten (tfb.tree_flatten) and tf.nest.pack_sequence_as (tfb.pack_sequence_as).
  • Adds tfp.experimental.bijectors.Sharded
  • Remove deprecated tfb.ScaleTrilL. Use tfb.FillScaleTriL instead.
  • Adds cls.parameter_properties() annotations for Bijectors.
  • Extend range tfb.Power to all reals for odd integer powers.
  • Infer the log-deg-jacobian of scalar bijectors using autodiff, if not otherwise specified.

• MCMC

  • MCMC diagnostics support arbitrary structures of states, not just lists.
  • remc_thermodynamic_integrals added to tfp.experimental.mcmc
  • Adds tfp.experimental.mcmc.windowed_adaptive_hmc
  • Adds an experimental API for initializing a Markov chain from a near-zero uniform distribution in unconstrained space. tfp.experimental.mcmc.init_near_unconstrained_zero
  • Adds an experimental utility for retrying Markov Chain initialization until an acceptable point is found. tfp.experimental.mcmc.retry_init
  • Shuffling experimental streaming MCMC API to slot into tfp.mcmc with a minimum of disruption.
  • Adds ThinningKernel to experimental.mcmc.
  • Adds experimental.mcmc.run_kernel driver as a candidate streaming-based replacement to mcmc.sample_chain

• VI

  • Adds build_split_flow_surrogate_posterior to tfp.experimental.vi to build structured VI surrogate posteriors from normalizing flows.
  • Adds build_affine_surrogate_posterior to tfp.experimental.vi for construction of ADVI surrogate posteriors from an event shape.
  • Adds build_affine_surrogate_posterior_from_base_distribution to tfp.experimental.vi to enable construction of ADVI surrogate posteriors with correlation structures induced by affine transformations.

• MAP/MLE

  • Added convenience method tfp.experimental.util.make_trainable(cls) to create trainable instances of distributions and bijectors.

• Math/linalg

  • Add trapezoidal rule to tfp.math.
  • Add tfp.math.log_bessel_kve.
  • Add no_pivot_ldl to experimental.linalg.
  • Add marginal_fn argument to GaussianProcess (see no_pivot_ldl).
  • Added tfp.math.atan_difference(x, y)
  • Add tfp.math.erfcx, tfp.math.logerfc and tfp.math.logerfcx
  • Add tfp.math.dawsn for Dawson's Integral.
  • Add tfp.math.igammaincinv, tfp.math.igammacinv.
  • Add tfp.math.sqrt1pm1.
  • Add LogitNormal.stddev_approx and LogitNormal.variance_approx
  • Add tfp.math.owens_t for the Owen's T function.
  • Add bracket_root method to automatically initialize bounds for a root search.
  • Add Chandrupatla's method for finding roots of scalar functions.

• Stats

  • tfp.stats.windowed_mean efficiently computes windowed means.
  • tfp.stats.windowed_variance efficiently and accurately computes windowed variances.
  • tfp.stats.cumulative_variance efficiently and accurately computes cumulative variances.
  • RunningCovariance and friends can now be initialized from an example Tensor, not just from explicit shape and dtype.
  • Cleaner API for RunningCentralMoments, RunningMean, RunningPotentialScaleReduction.

• STS

  • Speed up STS forecasting and decomposition using internal tf.function wrapping.
  • Add option to speed up filtering in LinearGaussianSSM when only the final step's results are required.
  • Variational Inference with Multipart Bijectors: example notebook with the Radon model.
  • Add experimental support for transforming any distribution into a preconditioning bijector.

• Other

  • Distributed inference example notebook
  • sanitize_seed is now available in the tfp.random namespace.
  • Add tfp.random.spherical_uniform.

Huge thanks to all the contributors to this release!

• Abhinav Upadhyay
• axch
• Brian Patton
• Chris Jewell
• Christopher Suter
• colcarroll
• Dave Moore
• ebrevdo
• Emily Fertig
• Harald Husum
• Ivan Ukhov
• jballe
• jburnim
• Jeff Pollock
• Jensun Ravichandran
• JulianWgs
• junpenglao
• jvdillon
• j-wilson
• kateslin
• Kristian Hartikainen
• ksachdeva
• langmore
• leben
• mattjj
• Nicola De Cao
• Pavel Sountsov
• paweller
• phawkins
• Prasanth Shyamsundar
• Rene Jean Corneille
• Samuel Marks
• scottzhu
• sharadmv
• siege
• Simon Dirmeier
• Srinivas Vasudevan
• Thomas Markovich
• ursk
• Uzair
• vanderplas
• yileiyang
• ZeldaMariet
• Zichun Ye