TensorFlow Probability 0.6.0

Release notes

This is the 0.6 release of TensorFlow Probability. It is
tested and stable against TensorFlow version 1.13.1.

Change notes

• Adds tfp.positive_semidefinite_kernels.RationalQuadratic
• Support float64 in tfpl.MultivariateNormalTriL.
• Add IndependentLogistic and IndependentPoisson distribution layers.
• Add make_value_setter interceptor to set values of Edward2 random variables.
• Implementation of Kalman Smoother, as a member function of LinearGaussianStateSpaceModel.
• Bijector caching is enabled only in one direction when executing in eager mode. May cause some performance regression in eager mode if repeatedly computing forward(x) or inverse(y) with the same x or y value.
• Handle rank-0/empty event_shape in tfpl.Independent{Bernoulli,Normal}.
• Run additional tests in eager mode.
• quantiles(x, n, ...) added to tfp.stats.
• Makes tensorflow_probability compatible with Tensorflow 2.0 TensorShape indexing.
• Use scipy.special functions when testing KL divergence for Chi, Chi2.
• Add methods to create forecasts from STS models.
• Add a MixtureSameFamily distribution layer.
• Add Chi distribution.
• Fix doc typo tfp.Distribution -> tfd.Distribution.
• Add Gumbel-Gumbel KL divergence.
• Add HalfNormal-HalfNormal KL divergence.
• Add Chi2-Chi2 KL divergence unit tests.
• Add Exponential-Exponential KL divergence unit tests.
• Add sampling test for Normal-Normal KL divergence.
• Add an IndependentNormal distribution layer.
• Added posterior_marginals to HiddenMarkovModel
• Add Pareto-Pareto KL divergence.
• Add LinearRegression component for structural time series models.
• Add dataset ops to the graph (or create kernels in Eager execution) during the python Dataset object creation instead doing it during Iterator creation time.
• Text messages HMC benchmark.
• Add example notebook encoding a switching Poisson process as an HMM for multiple changepoint detection.
• Require num_adaptation_steps argument to make_simple_step_size_update_policy.
• s/eight_hmc_schools/eight_schools_hmc/ in printed benchmark string.
• Add tfp.layers.DistributionLambda to enable plumbing tfd.Distribution instances through Keras models.
• Adding tfp.math.batch_interp_regular_1d_grid.
• Update description of fill_triangular to include an in-depth example.
• Enable bijector/distribution composition, eg, tfb.Exp(tfd.Normal(0,1)).
• linear and midpoint interpolation added to tfp.stats.percentile.
• Make distributions include only the bijectors they use.
• tfp.math.interp_regular_1d_grid added
• tfp.stats.correlation added (Pearson correlation).
• Update list of edward2 RVs to include recently added Distributions.
• Density of continuous Uniform distribution includes the upper endpoint.
• Add support for batched inputs in tfp.glm.fit_sparse.
• interp_regular_1d_grid added to tfp.math.
• Added HiddenMarkovModel distribution.
• Add Student's T Process.
• Optimize LinearGaussianStateSpaceModel by avoiding matrix ops when the observations are statically known to be scalar.
• stddev, cholesky added to tfp.stats.
• Add methods to fit structual time series models to data with variational inference and HMC.
• Add Expm1 bijector (Y = Exp(X) - 1).
• New stats namespace. covariance and variance added to tfp.stats
• Make all available MCMC kernels compatible with TransformedTransitionKernel.

Huge thanks to all the contributors to this release!

• Adam Wood
• Alexey Radul
• Anudhyan Boral
• Ashish Saxena
• Billy Lamberta
• Brian Patton
• Christopher Suter
• Cyril Chimisov
• Dave Moore
• Eugene Zhulenev
• Griffin Tabor
• Ian Langmore
• Jacob Burnim
• Jakub Arnold
• Jiahao Yao
• Jihun
• Jiming Ye
• Joshua V. Dillon
• Juan A. Navarro Pérez
• Julius Kunze
• Julius Plenz
• Kristian Hartikainen
• Kyle Beauchamp
• Matej Rizman
• Pavel Sountsov
• Peter Roelants
• Rif A. Saurous
• Rohan Jain
• Roman Ring
• Rui Zhao
• Sergio Guadarrama
• Shuhei Iitsuka
• Shuming Hu
• Srinivas Vasudevan
• Tabor473
• ValentinMouret
• Youngwook Kim
• Yuki Nagae