Infer.NET

Infer.NET user guide : : The Infer.NET Modelling API

Variable types and their distributions

The following table shows what types of variables are supported by Infer.NET, along with the distributions which are available for representing uncertainty in each type. You can create variables for each of these types using the static methods on Variable for each distribution. Some distributions with experimental quality band are not shown in this table.

Variable type	Restrictions	Distribution	Distribution Class	Example of use
bool	-	Bernoulli	Bernoulli	Two coins tutorial
double	-	Gaussian	Gaussian	Learning a Gaussian tutorial
double	between 0 and infinity	Gamma	Gamma	Learning a Gaussian tutorial
double	between 0 and 1	Beta	Beta	Clinical trial tutorial
double	between settable lower and upper bounds	Truncated Gaussian	TruncatedGaussian	-
double	between 0 and settable period length	Wrapped Gaussian	WrappedGaussian	-
double	between a lower bound and infinity	Pareto	Pareto	-
int	between 0 and D-1 inclusive	Discrete (categorical)	Discrete	Latent Dirichlet Allocation
int	between 0 and infinity	Poisson	Poisson	-
int	between 0 and N inclusive	Binomial	Binomial	-
enum	-	Discrete over enum values	DiscreteEnum	-
Vector	-	Vector Gaussian	VectorGaussian	Mixture of Gaussians tutorial
Vector	each element between 0 and 1, elements sum to 1	Dirichlet	Dirichlet	Latent Dirichlet Allocation
PositiveDefiniteMatrix	matrix is positive definite	Wishart	Wishart	Mixture of Gaussians tutorial
string	-	Probabilistic automaton	StringDistribution	Hello, Strings!
char	-	Discrete over char values	DiscreteChar	-
TDomain[]	T is a value-type distribution over a domain TDomain	Array of distributions considered as a distribution over an array	DistributionStructArray<T, TDomain>	-
TDomain[,]	T is a value-type distribution over a domain TDomain	2-D Array of distributions considered as a distribution over a 2-D array	DistributionStructArray2D<T, TDomain>	-
TDomain[]	T is a reference-type distribution over a domain TDomain	Array of distributions considered as a distribution over an array	DistributionRefArray<T, TDomain>	-
TDomain[,]	T is a reference-type distribution over a domain TDomain	2-D Array of distributions considered as a distribution over a 2-D array	DistributionRefArray2D<T, TDomain>	-
ISparseList<bool>	-	Sparse list of Bernoulli distributions considered as a distribution over a sparse list of bools	SparseBernoulliList	-
ISparseList<double>	elements between 0 and 1	Sparse list of Beta distributions considered as a distribution over a sparse list of doubles	SparseBetaList	-
ISparseList<double>	-	Sparse list of Gaussian distributions considered as a distribution over a sparse list of doubles	SparseGaussianList	-
ISparseList<double>	elements between 0 and infinity	Sparse list of Gamma distributions considered as a distribution over a sparse list of doubles	SparseGammaList	-
IList<int>	elements between 0 and N-1 inclusive	SparseBernoulliList where domain is list of indices with value true	BernoulliIntegerSubset	-
IFunction	-	Sparse Gaussian Process	SparseGP	Gaussian process classifier

Notes:

For descriptions of the Vector and PositiveDefiniteMatrix see the page on Vector and Matrix types.
DistributionRefArray<T, TDomain> can be used to represent a distribution over an arbitrarily deep jagged array domain. For example, the following alias (which can be copied and pasted into you code) represents a 2-deep array of Gaussians considered as a distribution over a 2-deep jagged array of double:
```
using GaussianArrayArray = DistributionRefArray<DistributionStructArray<Gaussian, double>, double[]>;
```
Posterior distributions for array variables can be passed back as either .NET arrays of distributions (for example Gaussian[][]), or as distribution arrays (for example GaussianArrayArray using the above alias). The former can be achieved by using (for example) Gaussian[][] as the type parameter in the Infer method. The latter is the native format which is therefore is more efficient and needs no casting or type parameter.
IFunction is an interface type which is used as the domain type for a SparseGP distribution. This interface has a single Evaluate method for a Vector domain:
```
double Evaluate(Vector v);
```