Module: tf.contrib.distributions

Defined in tensorflow/contrib/distributions/__init__.py.

Classes representing statistical distributions and ops for working with them.

See the Statistical Distributions (contrib) guide.

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.

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

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

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.

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)))).

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.

Other Members

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NOT_REPARAMETERIZED

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