A simple Rust crate for handling probability distributions, primarily intended for use with Bayesian inference.

All probability density functions (PDFs) implement the [Density] trait, providing common operations like:

• Evaluation: Compute probability density (non-normalized) for a given sample point
• Sampling: Generate random samples according to the given distribution
• Domain queries: Checking valid input ranges

The Domain trait represents types that define the valid input space for a PDF. A domain may be bounded, unbounded, or have a special structure.

Currently available domain types:

• MDomain - Bounded multivariate domains (hypercubes)
• UDomain - Unbounded multivariate domains
• SDomain - Univariate domains (for the use in univariate PDFs)

• MultivariateDensity Combines multiple independent univariate distributions into a multivariate distribution. Use when the individual dimensions are statistically independent.

• MultiNormalDensity A multivariate normal distribution with arbitrary covariance. Use when modeling correlated multi-dimensional data.

• ParticleDensity A non-parametric density represented by weighted particles / samples. Use for complex distributions that can't be expressed analytically or for particle filters.

For the univariate case, one can use the following distributions directly:

• ConstantDensity - A degenerate distribution at a fixed value
• CosineDensity - Cosine distribution over the interval [-π/2, π/2]
• LogUniformDensity - Uniform distribution in log-space (for positive values)
• NormalDensity - Normal distribution
• UniformDensity - Uniform distribution over an interval