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Classes representing statistical distributions and ops for working with them.
Use tfp.distributions instead.
Modules
bijectors module: Bijector Ops.
Classes
class Autoregressive: Autoregressive distributions.
class BatchReshape: The Batch-Reshaping distribution.
class Bernoulli: Bernoulli distribution.
class Beta: Beta distribution.
class BetaWithSoftplusConcentration: Beta with softplus transform of concentration1 and concentration0.
class Binomial: Binomial distribution.
class Categorical: Categorical distribution.
class Cauchy: The Cauchy distribution with location loc and scale scale.
class Chi2: Chi2 distribution.
class Chi2WithAbsDf: Chi2 with parameter transform df = floor(abs(df)).
class ConditionalDistribution: Distribution that supports intrinsic parameters (local latents).
class ConditionalTransformedDistribution: A TransformedDistribution that allows intrinsic conditioning.
class Deterministic: Scalar Deterministic distribution on the real line.
class Dirichlet: Dirichlet distribution.
class DirichletMultinomial: Dirichlet-Multinomial compound distribution.
class Distribution: A generic probability distribution base class.
class ExpRelaxedOneHotCategorical: ExpRelaxedOneHotCategorical distribution with temperature and logits.
class Exponential: Exponential distribution.
class ExponentialWithSoftplusRate: Exponential with softplus transform on rate.
class Gamma: Gamma distribution.
class GammaWithSoftplusConcentrationRate: Gamma with softplus of concentration and rate.
class Geometric: Geometric distribution.
class HalfNormal: The Half Normal distribution with scale scale.
class Independent: Independent distribution from batch of distributions.
class InverseGamma: InverseGamma distribution.
class InverseGammaWithSoftplusConcentrationRate: InverseGamma with softplus of concentration and rate.
class Kumaraswamy: Kumaraswamy distribution.
class Laplace: The Laplace distribution with location loc and scale parameters.
class LaplaceWithSoftplusScale: Laplace with softplus applied to scale.
class Logistic: The Logistic distribution with location loc and scale parameters.
class Mixture: Mixture distribution.
class MixtureSameFamily: Mixture (same-family) distribution.
class Multinomial: Multinomial distribution.
class MultivariateNormalDiag: The multivariate normal distribution on R^k.
class MultivariateNormalDiagPlusLowRank: The multivariate normal distribution on R^k.
class MultivariateNormalDiagWithSoftplusScale: MultivariateNormalDiag with diag_stddev = softplus(diag_stddev).
class MultivariateNormalFullCovariance: The multivariate normal distribution on R^k.
class MultivariateNormalTriL: The multivariate normal distribution on R^k.
class NegativeBinomial: NegativeBinomial distribution.
class Normal: The Normal distribution with location loc and scale parameters.
class NormalWithSoftplusScale: Normal with softplus applied to scale.
class OneHotCategorical: OneHotCategorical distribution.
class Poisson: Poisson distribution.
class PoissonLogNormalQuadratureCompound: PoissonLogNormalQuadratureCompound distribution.
class QuantizedDistribution: Distribution representing the quantization Y = ceiling(X).
class RegisterKL: Decorator to register a KL divergence implementation function.
class RelaxedBernoulli: RelaxedBernoulli distribution with temperature and logits parameters.
class RelaxedOneHotCategorical: RelaxedOneHotCategorical distribution with temperature and logits.
class ReparameterizationType: Instances of this class represent how sampling is reparameterized.
class SeedStream: Local PRNG for amplifying seed entropy into seeds for base operations.
class SinhArcsinh: The SinhArcsinh transformation of a distribution on (-inf, inf).
class StudentT: Student's t-distribution.
class StudentTWithAbsDfSoftplusScale: StudentT with df = floor(abs(df)) and scale = softplus(scale).
class TransformedDistribution: A Transformed Distribution.
class Uniform: Uniform distribution with low and high parameters.
class VectorDeterministic: Vector Deterministic distribution on R^k.
class VectorDiffeomixture: VectorDiffeomixture distribution.
class VectorExponentialDiag: The vectorization of the Exponential distribution on R^k.
class VectorLaplaceDiag: The vectorization of the Laplace distribution on R^k.
class VectorSinhArcsinhDiag: The (diagonal) SinhArcsinh transformation of a distribution on R^k.
class WishartCholesky: The matrix Wishart distribution on positive definite matrices.
class WishartFull: The matrix Wishart distribution on positive definite matrices.
Functions
assign_log_moving_mean_exp(...): Compute the log of the exponentially weighted moving mean of the exp.
assign_moving_mean_variance(...): Compute exponentially weighted moving {mean,variance} of a streaming value.
auto_correlation(...): Auto correlation along one axis.
estimator_head_distribution_regression(...): Creates a Head for regression under a generic distribution. (deprecated)
fill_triangular(...): Creates a (batch of) triangular matrix from a vector of inputs.
fill_triangular_inverse(...): Creates a vector from a (batch of) triangular matrix.
kl_divergence(...): Get the KL-divergence KL(distribution_a || distribution_b). (deprecated)
matrix_diag_transform(...): Transform diagonal of [batch-]matrix, leave rest of matrix unchanged.
moving_mean_variance(...): Compute exponentially weighted moving {mean,variance} of a streaming value.
normal_conjugates_known_scale_posterior(...): Posterior Normal distribution with conjugate prior on the mean.
normal_conjugates_known_scale_predictive(...): Posterior predictive Normal distribution w. conjugate prior on the mean.
percentile(...): Compute the q-th percentile of x.
quadrature_scheme_lognormal_gauss_hermite(...): Use Gauss-Hermite quadrature to form quadrature on positive-reals. (deprecated)
quadrature_scheme_lognormal_quantiles(...): Use LogNormal quantiles to form quadrature on positive-reals. (deprecated)
quadrature_scheme_softmaxnormal_gauss_hermite(...): Use Gauss-Hermite quadrature to form quadrature on K - 1 simplex. (deprecated)
quadrature_scheme_softmaxnormal_quantiles(...): Use SoftmaxNormal quantiles to form quadrature on K - 1 simplex. (deprecated)
reduce_weighted_logsumexp(...): Computes log(abs(sum(weight * exp(elements across tensor dimensions)))).
softplus_inverse(...): Computes the inverse softplus, i.e., x = softplus_inverse(softplus(x)).
tridiag(...): Creates a matrix with values set above, below, and on the diagonal.