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Module: tfp.experimental.substrates.numpy.distributions

Statistical distributions.

Classes

class Autoregressive: Autoregressive distributions.

class BatchReshape: The Batch-Reshaping distribution.

class Bernoulli: Bernoulli distribution.

class Beta: Beta distribution.

class Binomial: Binomial distribution.

class Blockwise: Blockwise distribution.

class Categorical: Categorical distribution over integers.

class Cauchy: The Cauchy distribution with location loc and scale scale.

class Chi: Chi distribution.

class Chi2: Chi2 distribution.

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 Empirical: Empirical distribution.

class ExpRelaxedOneHotCategorical: ExpRelaxedOneHotCategorical distribution with temperature and logits.

class Exponential: Exponential distribution.

class Gamma: Gamma distribution.

class GammaGamma: Gamma-Gamma distribution.

class GeneralizedPareto: The Generalized Pareto distribution.

class Geometric: Geometric distribution.

class Gumbel: The scalar Gumbel distribution with location loc and scale parameters.

class HalfCauchy: Half-Cauchy distribution.

class HalfNormal: The Half Normal distribution with scale scale.

class HiddenMarkovModel: Hidden Markov model distribution.

class Horseshoe: Horseshoe distribution.

class Independent: Independent distribution from batch of distributions.

class InverseGamma: InverseGamma distribution.

class InverseGaussian: Inverse Gaussian distribution.

class Kumaraswamy: Kumaraswamy distribution.

class LKJ: The LKJ distribution on correlation matrices.

class Laplace: The Laplace distribution with location loc and scale parameters.

class LinearGaussianStateSpaceModel: Observation distribution from a linear Gaussian state space model.

class LogNormal: The log-normal distribution.

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 MultivariateNormalFullCovariance: The multivariate normal distribution on R^k.

class MultivariateNormalLinearOperator: The multivariate normal distribution on R^k.

class MultivariateNormalTriL: The multivariate normal distribution on R^k.

class MultivariateStudentTLinearOperator: The [Multivariate Student's t-distribution](

class NegativeBinomial: NegativeBinomial distribution.

class Normal: The Normal distribution with location loc and scale parameters.

class OneHotCategorical: OneHotCategorical distribution.

class Pareto: Pareto distribution.

class Poisson: Poisson distribution.

class ProbitBernoulli: ProbitBernoulli 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 Sample: Sample distribution via independent draws.

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 TransformedDistribution: A Transformed Distribution.

class Triangular: Triangular distribution with low, high and peak parameters.

class TruncatedNormal: The Truncated Normal 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 Wishart: The matrix Wishart distribution on positive definite matrices.

class Zipf: Zipf distribution.

Functions

fill_triangular(...): Creates a (batch of) triangular matrix from a vector of inputs. (deprecated)

fill_triangular_inverse(...): Creates a vector from a (batch of) triangular matrix. (deprecated)

kl_divergence(...): Get the KL-divergence KL(distribution_a || distribution_b).

matrix_diag_transform(...): Transform diagonal of [batch-]matrix, leave rest of matrix unchanged. (deprecated)

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.

quadrature_scheme_lognormal_quantiles(...): Use LogNormal quantiles to form quadrature on positive-reals.

quadrature_scheme_softmaxnormal_gauss_hermite(...): Use Gauss-Hermite quadrature to form quadrature on K - 1 simplex.

quadrature_scheme_softmaxnormal_quantiles(...): Use SoftmaxNormal quantiles to form quadrature on K - 1 simplex.

reduce_weighted_logsumexp(...): Computes log(abs(sum(weight * exp(elements across tensor dimensions)))). (deprecated)

softplus_inverse(...): Computes the inverse softplus, i.e., x = softplus_inverse(softplus(x)). (deprecated)

tridiag(...): Creates a matrix with values set above, below, and on the diagonal. (deprecated)

Other Members

• FULLY_REPARAMETERIZED
• NOT_REPARAMETERIZED