Represents a sparse 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'.

BetaEstimator

Estimates a Beta distribution from samples.

BurnInAccumulator<T>

Wraps an accumulator, discarding the first BurnIn samples.

CollectionElementMappingInfo

Element mapping information for the product of collection distributions

ConstantFunction

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

Dirichlet

A Dirichlet distribution on probability vectors.

DirichletEstimator

Estimates a Dirichlet distribution from samples.

Discrete

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

DiscreteEnum<TEnum>

A discrete distribution over the values of an enum.

DiscreteEstimator

Estimates a discrete distribution from samples.

Distribution

Static class which implements useful functions on distributions.

Distribution<T>

Static class which implements useful functions on distributions.

DistributionArray<T>

A distribution over an array, where each element is independent and has distribution type T

DistributionArray<T, DomainType>

A distribution over an array of type DomainType, where each element is independent and has distribution of type T

DistributionArray2D<T>

A distribution over a 2D array, where each element is independent and has distribution type T

DistributionArray2D<T, DomainType>

A distribution over an array of type DomainType, where each element is independent and has distribution of type T

DistributionFileArray<T, DomainType>

A distribution over an array of type DomainType, where each element is independent and has distribution of type T, all stored in a file.

DistributionRefArray<T, DomainType>

A distribution over an array of type DomainType, where each element is independent and has distribution of type T

DistributionRefArray2D<T, DomainType>

A distribution over a 2D array of type DomainType, where each element is independent and has distribution of type T

DistributionStructArray<T, DomainType>

A distribution over an array of type DomainType, where each element is independent and has distribution of type T

DistributionStructArray2D<T, DomainType>

A distribution over a 2D array of type DomainType, where each element is independent and has distribution of type T

EstimatorFactory

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

GammaEstimator

Estimates a Gamma distribution from samples.

GammaPowerEstimator

Estimates a GammaPower distribution from samples.

GaussianEstimator

Estimates a Gaussian distribution from samples.

GaussianProcess

A base class for Gaussian process distributions

GenericDiscreteBase<T, TThis>

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

GibbsMarginal<TDist, T>

Gibbs marginal - wraps underlying estimator, provides burn-in and thinning, and maintains thinned samples and conditionals

ImproperDistributionException

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

InnerQuantiles

Represents a distribution using the quantiles at probabilities (1,...,n)/(n+1)

IrregularQuantiles

Represents a distribution using the quantiles at arbitrary probabilities.

LeftTruncatedPoisson

Left-truncated Com-Poisson distribution.

LinearSpline

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.

ListDistribution<TElement, TElementDistribution>

Represents a distribution over List<T> that use a weighted finite state automaton as the underlying weight function.

ListDistribution<TList, TElement, TElementDistribution>

Represents a distribution over lists that use a weighted finite state automaton as the underlying weight function.

ListDistribution<TList, TElement, TElementDistribution, TThis>

A base class for distributions over lists that use a weighted finite state automaton as the underlying weight function.

Mixture<TDist, TDomain, TThis>

A mixture of distributions of the same type

MixtureEstimator<TDist>

An estimator which is a mixture of distributions of the same type

OuterQuantiles

Represents a distribution using the quantiles at probabilities (0,...,n-1)/(n-1)

PointMass<T>

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.

PointMassEstimator<T>

Estimates a point mass distribution from a sample or point mass distribution.

PoissonEstimator

Estimates a Poisson distribution from samples.

QuantileEstimator

Subsamples data to provide accurate estimation of quantiles.

Rank1Pot

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.

SequenceDistribution<TSequence, TElement, TElementDistribution, TSequenceManipulator, TAutomaton, TWeightFunction, TWeightFunctionFactory, TThis>

A base class for implementations of distributions over sequences.

SequenceDistributionFormats

A collection of sequence distribution formats.

Represents a sparse list of Bernoulli distributions, optimized for the case where many share the same parameter value. The class supports an approximation tolerance which allows elements close to the common value to be automatically reset to the common value.

SparseBetaList

Represents a sparse list of Beta distributions, optimized for the case where many share the same parameter value. The class supports an approximation tolerance which allows elements close to the common value to be automatically reset to the common value.

SparseDistributionList<TDist, TDomain, TThis>

Abstract base class for a homogeneous sparse list of distributions. The class supports an approximation tolerance which allows elements close to the common value to be automatically reset to the common value. The list implements the interfaces which allow these distributions to participate in message passing.

SparseGammaList

Represents a sparse list of Gamma distributions, optimized for the case where many share the same parameter value. The class supports an approximation tolerance which allows elements close to the common value to be automatically reset to the common value.

SparseGaussianList

Represents a sparse list of Gaussian distributions, optimized for the case where many share the same parameter value. The class supports an approximation tolerance which allows elements close to the common value to be automatically reset to the common value.

SparseGP

A Gaussian Process distribution over functions, represented by a GP prior times a set of regression likelihoods on basis points.

SparseGPFixed

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

StringDistribution

Represents a distribution over strings that uses a weighted finite state automaton as the underlying weight function.

TruncatableDistribution<T>

TruncatedDistribution

TruncatedGaussianEstimator

Estimates a TruncatedGaussian distribution from samples.

TruncatedPoisson

Left-and-right-truncated Com-Poisson distribution.

UnnormalizedDiscrete

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

VectorGaussian

Represents a multivariate Gaussian distribution.

VectorGaussianEstimator

Estimates a Gaussian distribution from samples.

VectorGaussianMoments

Represents a multivariate Gaussian distribution.

Wishart

A Wishart distribution on positive definite matrices.

WishartEstimator

Estimates a Wishart distribution from samples.

WordStrings

Sets of strings of whole words.

Structs

Bernoulli

Represents a distribution on a binary variable.

Beta

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

Binomial

Binomial distribution over the integers [0,n]

ConjugateDirichlet

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

DiscreteChar

Represents a distribution over characters.

Gamma

A Gamma distribution on positive reals.

GammaPower

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

Gaussian

Represents a one-dimensional Gaussian distribution.

ImmutableDiscreteChar

Represents a distribution over characters.

ImmutableDiscreteChar.CharRange

Represents a range of characters, with an associated probability.

NonconjugateGaussian

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

Pareto

A Pareto distribution over the real numbers from lowerBound to infinity.

Poisson

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

TruncatedGamma

A distribution over real numbers between an upper and lower bound. If LowerBound=0 and UpperBound=Inf, it reduces to an ordinary Gamma distribution.

TruncatedGaussian

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

WrappedGaussian

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

Interfaces

Accumulator<T>

Indicates support for adding an item to a distribution estimator

CanComputePower<TDist>

Supports computing the value of the current instance raised to a power.

CanComputeProduct<T>

Supports computing the product of the current instance and another value that has the same type as the product.

CanComputeProduct<TOther, TResult>

Supports computing the product of the current instance and another value.

CanComputeRatio<T>

Supports computing the ratio of the current instance and another value that has the same type as the ratio.

CanComputeRatio<TDenominator, TResult>

Supports computing the ratio of the current instance and another value.

CanCopyWithAllElementsSetTo<T, TDist>

Whether the distribution supports creating copies with all elements set to duplicates of the same value.

CanCopyWithMean<MeanType, TDist>

Whether the distribution supports creating copies with different mean value.

CanCopyWithMeanAndVariance<MeanType, VarType, TDist>

Whether the distribution supports creating copies with different mean and variance.

CanCreatePartialUniform<TDist>

Whether the distribution can create another distribution of the same type and with the same support, but partially uniform over said support.

CanCreatePointMass<T, TDist>

Whether the distribution supports being a point mass.

CanCreateUniform<TDist>

Whether the distribution can be uniform.

CanEnumerateSupport<T>

Whether the distribution supports enumeration over the support - i.e. enumeration over the domain values with non-zero mass.

CanGetAverageLog<T>

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

CanGetLogAverageOf<T>

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

CanGetLogAverageOfPower<T>

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

CanGetLogNormalizer

Whether the distribution can compute its normalizer.

CanGetLogProb<T>

Whether the distribution supports evaluation of its density

CanGetLogProbPrep<DistributionType, T>

Whether the distribution supports preallocation of a workspace for density evaluation

CanGetMean<MeanType>

Whether the distribution supports retrieval of a mean value

CanGetMeanAndVariance<MeanType, VarType>

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

CanGetMeanAndVarianceOut<MeanType, VarType>

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

CanGetMode<ModeType>

Whether the distribution supports retrieval of the most probable value

CanGetProbLessThan<T>

CanGetQuantile<T>

CanGetVariance<VarType>

Whether the distribution supports retrieval of a variance value

CanSamplePrep<DistributionType, T>

Whether the distribution supports preallocation of a workspace for sampling

CanSetMean<MeanType>

Whether the distribution supports setting of its mean value

CanSetMeanAndVariance<MeanType, VarType>

Whether the distribution supports the joint setting of mean and variance

Estimator<T>

Indicates support for retrieving an estimated distribution

HasPoint<T>

Whether the distribution supports being a point mass

ICanTruncateLeft<TDomain>

Whether the distribution can be left-truncated.

ICanTruncateRight<TDomain>

Whether the distribution can be right truncated.

ICollectionDistribution

Interface to allow untyped access to collection distribution

ICollectionDistribution<TElement, TElementDist>

Collection distribution interface

IDistribution

Base type for all distributions that doesn't specify over which domain distribution is defined.

IDistribution<T>

Distribution interface

IFunction

Function interface - used for distributions over a function domain

IGaussianProcess

Basic GP interface

IImmutableDistribution

Base type for all immutable distributions that doesn't specify over which domain distribution is defined.

IImmutableDistribution<T, TThis>

Immutable distribution interface

IsDistributionWrapper

Marker interface for classes which wrap distributions

ITruncatableDistribution<T>

Sampleable<T>

Whether the distribution supports sampling

SettableToPartialUniform<TDist>

Whether the distribution can be set to be uniform over the support of another distribution.

SettableToUniform

Whether the distribution can be set to uniform

Summable<T>

Supports computing the weighted sum of the current instance and another value that has the same type as the result.

Summable<TOther, TResult>

Supports computing the weighted sum of the current instance and another value.

SummableExactly<T>

Indicates that the computed weighted sum is exact.

SummableExactly<TOther, TResult>

Indicates that the computed weighted sum is exact.

Enums

ConjugateDirichlet.ApproximationMethod

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

ImmutableDiscreteChar.CharClasses

Delegates

CreateEstimatorMethod

Evaluator<DistributionType, T>

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.

Sampler<T>

Delegate type for sampling

Sampler<DistributionType, T>

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.