[tune](deps): Bump tensorflow-probability from 0.11.1 to 0.14.0 in /python/requirements

[dependabot]

Bumps tensorflow-probability from 0.11.1 to 0.14.0.

Release notes

Sourced from tensorflow-probability's releases.

TensorFlow Probability 0.13.0

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.

... (truncated)

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