Approximates an empirical distribution with a theoretical one

fit_mle(d, x, ...)

Arguments

d

A probability distribution object such as those created by a call to Bernoulli(), Beta(), or Binomial().

x

A vector of data to compute the likelihood.

...

Unused. Unevaluated arguments will generate a warning to catch mispellings or other possible errors.

Value

A distribution (the same kind as d) where the parameters are the MLE estimates based on x.

Examples

X <- Normal() fit_mle(X, c(-1, 0, 0, 0, 3))
#> Normal distribution (mu = 0.4, sigma = 1.51657508881031)