make_flat_prior: Make a flat prior for maximum likelihood estimation

In davharris/rosalia: Exact inference for small binary Markov networks

Description

Make a flat prior for maximum likelihood estimation

Usage

make_flat_prior(...)

Arguments

...	Currently not used

Value

A prior object, which is a list of two functions:

log_d	A function for calculating the log of the prior density for each element of a vector. With a uniform prior, the log-density is always zero.
log_grad	A function for calculating the gradient of the log-density for each element of a vector. For the uniform prior, this is always 0. With a uniform prior, this gradient is always zero.

See Also

make_logistic_prior

Examples

p = make_flat_prior()
curve(p$log_d(x), from = -5, to = 5)
curve(p$log_grad(x), from = -5, to = 5)

davharris/rosalia documentation built on May 14, 2019, 9:29 p.m.