Nonparametric distribution

A nonparametric distribution as a <dist_spec>, defined directly by its probability mass function. The PMF can instead be left uncertain by passing a Dirichlet() prior.

Usage

NonParametric(pmf, ...)

Arguments

pmf

Probability mass function, as a zero-indexed numeric vector (the first entry is the mass at zero) or a <dist_spec> (e.g. from Dirichlet()). A numeric vector is normalised to sum to one.

...

Limits of the distribution, passed to bound_dist().

Value

A <dist_spec>.

Details

A distribution constructed with a Dirichlet() prior has no concrete PMF, in the same way that Gamma(shape = Normal(3, 0.5), rate = 2) has an uncertain shape in place of a fixed value. get_pmf() errors and has_uncertainty() returns TRUE until fix_parameters() resolves the prior into a PMF (e.g. with strategy = "mean").

See also

Distributions for an overview and the other distributions.

Examples

NonParametric(c(0.1, 0.3, 0.2, 0.4))
#> - nonparametric distribution
#>   PMF: [0.1 0.3 0.2 0.4]

# With a Dirichlet prior (PMF left uncertain)
NonParametric(pmf = Dirichlet(c(1, 1, 1, 1)))
#> - nonparametric distribution:
#>   pmf:
#>     - dirichlet distribution:
#>       alpha:
#>         1 1 1 1