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.

Variable type	Restrictions	Distribution	Distribution Class	Example of use
bool	-	Bernoulli	Bernoulli	Two coins tutorial
double	-	Gaussian	Gaussian	Learning a Gaussian tutorial
	between 0 and infinity	Gamma	Gamma	Learning a Gaussian tutorial
	between 0 and 1	Beta	Beta	Clinical trial tutorial
	between settable lower and upper bounds	Truncated Gaussian	TruncatedGaussian	-
	between 0 and settable period length	Wrapped Gaussian	WrappedGaussian	-
int	between 0 and D-1 inclusive	Discrete (categorical)	Discrete	Latent Dirichlet Allocation
	between 0 and infinity	Poisson	Poisson	-
enum	-	Discrete over enum values	DiscreteEnum	-
Vector	-	Vector Gaussian	VectorGaussian	Mixture of Gaussians tutorial
	each element between 0 and 1, elements sum to 1	Dirichlet	Dirichlet	Latent Dirichlet Allocation
PositiveDefiniteMatrix	-	Wishart	Wishart	Mixture of Gaussians tutorial
IFunction	-	Sparse Gaussian Process	SparseGP	Gaussian process classifier

Notes:

For descriptions of the Vector and PositiveDefiniteMatrix see the page on Vector and Matrix types.
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);