Wraps a list of accumulators, adding each sample to all of them.

Estimator for a 2-D DistributionArray type, where the samples are distributions

Estimator for a 2-D DistributionArray type.

Useful static methods relating to array estimators

Static class which implements useful functions on estimator arrays.

Estimator for a DistributionArray type where the sample type is a distribution

Estimator for a DistributionArray type.

Estimates a Bernoulli distribution from samples.

Represents a list of Bernoulli distributions considered as a distribution over a variable-sized list of integers, which are the indices of elements in the boolean list with value 'true'

Estimates a Beta distribution from samples.

Wraps an accumulator, discarding the first BurnIn samples.

Conditional List

Class implementing the constant function. Used as a domain prototype for distributions over functions

A Dirichlet distribution on probability vectors.

Estimates a Dirichlet distribution from samples.

An arbitrary distribution over integers [0,D-1].

A discrete distribution over characters.

A discrete distribution over the values of an enum.

Estimates a discrete distribution from samples.

Static class which implements useful functions on distributions.

Static class which implements useful functions on distributions.

Estimator factor. Given a distribution instance, create a compatible estimator instance

Estimates a Gamma distribution from samples.

Estimates a Gaussian distribution from samples.

A base class for Gaussian process distributions

A generic base class for discrete distributions over a type T.

Exception thrown when a distribution is improper and its expectations need to be computed.

Very simple 1-D linear spline class which implements IFunction. Assumes knots at regular positions - given by a start and increment. The vector of knot values defines how many knots.

A mixture of distributions of the same type

A point mass, which is the 'distribution' you get for an observed variable. All the probability mass is at the point given by observed value.

Estimates a Poisson distribution from samples.

Rank 1 potential for a sparse GP. This low rank parameterisation is used for messages flowing from a SparseGP evaluation factor to a function variable.

Sample List

Represents a list of Bernoulli distributions, optimised for the case where many share the same probability of being true. This class can be used as a distribution over a fixed-sized list of booleans or sparsely as a distribution over a variable-sized list of integers, which are the indices of elements in the boolean list with value 'true'.

Base class for BernoulliIntegerSubset and SparseBernoulliList.

Represents a list of Beta distributions, optimised for the case where many share the same pseudocount values.

Represents a sparse list of Gamma distributions, optimised for the case where many share the same parameterisation.

Represents a sparse list of Gaussian distributions, optimised for the case where many share the same parameterisation.

A Gaussian Process distribution over functions, represented by a GP prior times a set of regression likelihoods on basis points. The GP prior and basis point locations are stored in FixedParameters. The regression likelihoods are stored as a single VectorGaussian called InducingDist. IncludePrior=false does not include the prior in the distribution (i.e. the distribution is degenerate). If pointFunc != null, the distribution is a point mass. If InducingDist is uniform and IncludePrior is false, the distribution is uniform. The GP prior is assumed to be non-uniform.

This class maintains all the fixed parameters for a sparse GP - i.e. parameters which the inference does not change. All SparseGP messages can refer to a single SparseGPFixed class, and cloning of SparseGP instances will just copy the reference

Represents a discrete distribution in the log domain without explicit normalization.

Represents a multivariate Gaussian distribution.

Estimates a Gaussian distribution from samples.

A Wishart distribution on positive definite matrices.

Estimates a Wishart distribution from samples.

Represents a distribution on a binary variable.

A Beta distribution over the interval [0,1].

Binomial distribution over the integers [0,n]

Represents the distribution proportion to x^{Shape-1} exp(-Rate*x) / B(x,D)^K where B(x,D)=Gamma(x)^D/Gamma(D*x)

A Gamma distribution on positive reals.

The distribution of a Gamma variable raised to a power. The Weibull distribution is a special case.

Represents a one-dimensional Gaussian distribution.

Nonconjugate Gaussian messages for VMP. The mean has a Gaussian distribution and the variance a Gamma distribution.

A Poisson distribution over the integers [0,infinity).

A distribution over real numbers between an upper and lower bound. If both bounds are infinite, it reduces to an ordinary Gaussian distribution.

A Gaussian distribution on a periodic domain, such as angles between 0 and 2*pi.

Indicates support for adding an item to a distribution estimator

Whether the distribution supports the expected logarithm of one instance under another

Whether the distribution can compute the expectation of another distribution's value.

Whether the distribution can compute the expectation of another distribution raised to a power.

Whether the distribution can compute its normalizer.

Whether the distribution supports evaluation of its density

Whether the distribution supports preallocation of a workspace for density evaluation

Whether the distribution supports retrieval of a mean value

Whether the distribution supports the joint getting of mean and variance where the mean and variance are reference types

Whether the distribution supports the joint getting of mean and variance where the mean and variance are returned as 'out' argiments

Whether the distribution supports retrieval of a variance value

Whether the distribution supports preallocation of a workspace for sampling

Whether the distribution supports setting of its mean value

Whether the distribution supports the joint setting of mean and variance

Indicates support for retrieving an estimated distribution

Whether the distribution supports being a point mass

Distribution interface

Function interface - used for distributions over a function domain

Basic GP interface

Marker interface for classes which wrap distributions

Whether the distribution supports sampling

Whether the distribution can be set to uniform

Delegate type for evaluating log densities. This is used for distributions such as VectorGaussian which have a large memory footprint. If a distribution supports CanGetLogProbPrep DistributionType, T , then it can return a delegate of this type to do evaluations without recreating a workspace each time.

Delegate type for sampling

Delegate type for sampling a distribution. This is used for distributions such as VectorGaussian which have a large memory footprint. If a distribution supports CanSamplePrep DistributionType, T , then it can return a delegate of this type to do successive sampling without recreating a workspace each time.

Approximation method to use for non-analytic expectations. Asymptotic: use expectations under the approximating Gamma distribution GaussHermiteQuadrature: Use Gauss-Hermite quadrature with 32 quadrature points ClenshawCurtisQuadrature: Use Clenshaw Curtis quadrature with an adaptive number of quadrature points