# Module: oryx.distributions

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Module for probability distributions and related functions.

## Classes

`class Autoregressive`: Autoregressive distributions.

`class BatchBroadcast`: A distribution that broadcasts an underlying distribution's batch shape.

`class BatchReshape`: The Batch-Reshaping distribution.

`class Bates`: Bates distribution.

`class Bernoulli`: Bernoulli distribution.

`class Beta`: Beta distribution.

`class BetaBinomial`: Beta-Binomial compound distribution.

`class BetaQuotient`: BetaQuotient 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 CholeskyLKJ`: The CholeskyLKJ distribution on cholesky factors of correlation matrices.

`class DeterminantalPointProcess`: Determinantal point process (DPP) 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 DoublesidedMaxwell`: Double-sided Maxwell distribution.

`class Empirical`: Empirical distribution.

`class ExpGamma`: ExpGamma distribution.

`class ExpInverseGamma`: ExpInverseGamma distribution.

`class ExpRelaxedOneHotCategorical`: ExpRelaxedOneHotCategorical distribution with temperature and logits.

`class Exponential`: Exponential distribution.

`class ExponentiallyModifiedGaussian`: Exponentially modified Gaussian distribution.

`class FiniteDiscrete`: The finite discrete distribution.

`class Gamma`: Gamma distribution.

`class GammaGamma`: Gamma-Gamma distribution.

`class GaussianProcess`: Marginal distribution of a Gaussian process at finitely many points.

`class GaussianProcessRegressionModel`: Posterior predictive distribution in a conjugate GP regression model.

`class GeneralizedExtremeValue`: The scalar GeneralizedExtremeValue distribution.

`class GeneralizedNormal`: The Generalized Normal 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 HalfStudentT`: Half-Student's t distribution.

`class HiddenMarkovModel`: Hidden Markov model distribution.

`class Horseshoe`: Horseshoe distribution.

`class Independent`: Independent distribution from batch of distributions.

`class Inflated`: A mixture of a point-mass and another distribution.

`class InverseGamma`: InverseGamma distribution.

`class InverseGaussian`: Inverse Gaussian distribution.

`class JohnsonSU`: Johnson's SU-distribution.

`class JointDistribution`: Joint distribution over one or more component distributions.

`class JointDistributionCoroutine`: Joint distribution parameterized by a distribution-making generator.

`class JointDistributionCoroutineAutoBatched`: Joint distribution parameterized by a distribution-making generator.

`class JointDistributionNamed`: Joint distribution parameterized by named distribution-making functions.

`class JointDistributionNamedAutoBatched`: Joint distribution parameterized by named distribution-making functions.

`class JointDistributionSequential`: Joint distribution parameterized by distribution-making functions.

`class JointDistributionSequentialAutoBatched`: Joint distribution parameterized by distribution-making functions.

`class Kumaraswamy`: Kumaraswamy distribution.

`class LKJ`: The LKJ distribution on correlation matrices.

`class LambertWDistribution`: Implements a general heavy-tail Lambert W x F distribution.

`class LambertWNormal`: Implements a location-scale heavy-tail Lambert W x Normal distribution.

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

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

`class LogLogistic`: The log-logistic distribution.

`class LogNormal`: The log-normal distribution.

`class Logistic`: The Logistic distribution with location `loc` and `scale` parameters.

`class LogitNormal`: The logit-normal distribution.

`class MarkovChain`: Distribution of a sequence generated by a memoryless process.

`class Masked`: A distribution that masks invalid underlying distributions.

`class MatrixNormalLinearOperator`: The Matrix Normal distribution on `n x p` matrices.

`class MatrixTLinearOperator`: The Matrix T distribution on `n x p` matrices.

`class Mixture`: Mixture distribution.

`class MixtureSameFamily`: Mixture (same-family) distribution.

`class Moyal`: The Moyal distribution with location `loc` and `scale` parameters.

`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 NormalInverseGaussian`: Normal Inverse Gaussian distribution.

`class OneHotCategorical`: OneHotCategorical distribution.

`class OrderedLogistic`: Ordered logistic distribution.

`class PERT`: Modified PERT distribution for modeling expert predictions.

`class Pareto`: Pareto distribution.

`class PlackettLuce`: Plackett-Luce distribution over permutations.

`class Poisson`: Poisson distribution.

`class PoissonLogNormalQuadratureCompound`: `PoissonLogNormalQuadratureCompound` distribution.

`class PowerSpherical`: The Power Spherical distribution over unit vectors on `S^{n-1}`.

`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`: Distribution over IID samples of a given shape.

`class SigmoidBeta`: SigmoidBeta Distribution.

`class SinhArcsinh`: The SinhArcsinh transformation of a distribution on `(-inf, inf)`.

`class Skellam`: Skellam distribution.

`class SphericalUniform`: The uniform distribution over unit vectors on `S^{n-1}`.

`class StoppingRatioLogistic`: Stopping ratio logistic distribution.

`class StudentT`: Student's t-distribution.

`class StudentTProcess`: Marginal distribution of a Student's T process at finitely many points.

`class StudentTProcessRegressionModel`: StudentTProcessRegressionModel.

`class TransformedDistribution`: A Transformed Distribution.

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

`class TruncatedCauchy`: The Truncated Cauchy distribution.

`class TruncatedNormal`: The Truncated Normal distribution.

`class TwoPieceNormal`: The Two-Piece Normal distribution.

`class Uniform`: Uniform distribution with `low` and `high` parameters.

`class VariationalGaussianProcess`: Posterior predictive of a variational Gaussian process.

`class VectorDeterministic`: Vector `Deterministic` distribution on `R^k`.

`class VonMises`: The von Mises distribution over angles.

`class VonMisesFisher`: The von Mises-Fisher distribution over unit vectors on `S^{n-1}`.

`class Weibull`: The Weibull distribution with 'concentration' and `scale` parameters.

`class WishartLinearOperator`: The matrix Wishart distribution on positive definite matrices.

`class WishartTriL`: The matrix Wishart distribution parameterized with Cholesky factors.

`class ZeroInflatedNegativeBinomial`: A mixture of a point-mass and another distribution.

`class Zipf`: Zipf distribution.

## Functions

`independent_joint_distribution_from_structure(...)`: Turns a (potentially nested) structure of dists into a single dist.

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

`mvn_conjugate_linear_update(...)`: Computes a conjugate normal posterior for a Bayesian linear regression.

`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_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.

FULLY_REPARAMETERIZED Instance of `oryx.distributions.ReparameterizationType`
NOT_REPARAMETERIZED Instance of `oryx.distributions.ReparameterizationType`

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