By default it will discretise all the distributions it can discretise (i.e. those with finite support and constant parameters).

## Usage

discretise(x, strict = TRUE)

discretize(x, strict = TRUE)

## Arguments

x

A <dist_spec>

strict

Logical; If TRUE (default) an error will be thrown if a distribution cannot be discretised (e.g., because no finite maximum has been specified or parameters are uncertain). If FALSE then any distribution that cannot be discretised will be returned as is.

## Value

A <dist_spec> where all distributions with constant parameters are nonparametric.

## Details

Discretise a <dist_spec>

## Examples

# A fixed gamma distribution with mean 5 and sd 1.
dist1 <- Gamma(mean = 5, sd = 1, max = 20)

# An uncertain lognormal distribution with mean 3 and sd 2
dist2 <- LogNormal(mean = Normal(3, 0.5), sd = Normal(2, 0.5), max = 20)
#> Warning: Uncertain lognormal distribution specified in terms of parameters that are not the "natural" parameters of the distribution (meanlog, sdlog). Converting using a crude and very approximate method that is likely to produce biased results. If possible, it is preferable to specify the distribution directly in terms of the natural parameters.

# The maxf the sum of two distributions
discretise(dist1 + dist2, strict = FALSE)
#> Composite distribution:
#> - nonparametric distribution
#>   PMF: [8e-11 2.3e-05 0.0056 0.078 0.26 0.34 0.22 0.076 0.016 0.0022 0.00022 1.7e-05 1.1e-06 5.5e-08 2.4e-09 9.2e-11 3.2e-12 9.7e-14 2.8e-15 0 0]
#> - lognormal distribution (max: 20):
#>   meanlog:
#>     - normal distribution:
#>       mean:
#>         0.91
#>       sd:
#>         0.31
#>   sdlog:
#>     - normal distribution:
#>       mean:
#>         0.61
#>       sd:
#>         0.25