tf.contrib.distributions
Classes representing statistical distributions and ops for working with them.
See the Statistical Distributions (contrib) guide.
class Bernoulli
: Bernoulli distribution.
class BernoulliWithSigmoidP
: Bernoulli with p = sigmoid(p)
.
class Beta
: Beta distribution.
class BetaWithSoftplusAB
: Beta with softplus transform on a
and b
.
class Binomial
: Binomial distribution.
class Categorical
: Categorical distribution.
class Chi2
: The Chi2 distribution with degrees of freedom df.
class Chi2WithAbsDf
: Chi2 with parameter transform df = floor(abs(df))
.
class Dirichlet
: Dirichlet distribution.
class DirichletMultinomial
: DirichletMultinomial mixture distribution.
class Distribution
: A generic probability distribution base class.
class Exponential
: The Exponential distribution with rate parameter lam.
class ExponentialWithSoftplusLam
: Exponential with softplus transform on lam
.
class Gamma
: The Gamma
distribution with parameter alpha and beta.
class GammaWithSoftplusAlphaBeta
: Gamma with softplus transform on alpha
and beta
.
class InverseGamma
: The InverseGamma
distribution with parameter alpha and beta.
class InverseGammaWithSoftplusAlphaBeta
: Inverse Gamma with softplus applied to alpha
and beta
.
class Laplace
: The Laplace distribution with location and scale > 0 parameters.
class LaplaceWithSoftplusScale
: Laplace with softplus applied to scale
.
class Mixture
: Mixture distribution.
class Multinomial
: Multinomial distribution.
class MultivariateNormalCholesky
: The multivariate normal distribution on R^k
.
class MultivariateNormalDiag
: The multivariate normal distribution on R^k
.
class MultivariateNormalDiagPlusVDVT
: The multivariate normal distribution on R^k
.
class MultivariateNormalDiagWithSoftplusStDev
: MultivariateNormalDiag with diag_stddev = softplus(diag_stddev)
.
class MultivariateNormalFull
: The multivariate normal distribution on R^k
.
class Normal
: The scalar Normal distribution with mean and stddev parameters mu, sigma.
class NormalWithSoftplusSigma
: Normal with softplus applied to sigma
.
class Poisson
: Poisson distribution.
class QuantizedDistribution
: Distribution representing the quantization Y = ceiling(X)
.
class RegisterKL
: Decorator to register a KL divergence implementation function.
class StudentT
: Student's t distribution with degree-of-freedom parameter df.
class StudentTWithAbsDfSoftplusSigma
: StudentT with df = floor(abs(df))
and sigma = softplus(sigma)
.
class TransformedDistribution
: A Transformed Distribution.
class Uniform
: Uniform distribution with a
and b
parameters.
class WishartCholesky
: The matrix Wishart distribution on positive definite matrices.
class WishartFull
: The matrix Wishart distribution on positive definite matrices.
bijector
module: Bijector Ops. See the Random variable transformations (contrib) guide.
kl(...)
: Get the KL-divergence KL(dist_a || dist_b).
matrix_diag_transform(...)
: Transform diagonal of [batch-]matrix, leave rest of matrix unchanged.
normal_conjugates_known_sigma_posterior(...)
: Posterior Normal distribution with conjugate prior on the mean.
normal_conjugates_known_sigma_predictive(...)
: Posterior predictive Normal distribution w. conjugate prior on the mean.
softplus_inverse(...)
: Computes the inverse softplus, i.e., x = softplus_inverse(softplus(x)).
Defined in tensorflow/contrib/distributions/__init__.py
.
© 2017 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/contrib/distributions