metropolis: Metropolis sampling of a continuous distribution

In bayesball/TeachBayes: Teaching Bayesian Inference

Description

Implements Metropolis sampling for an arbitrary continuous probability distribution

Usage

  metropolis(logpost, current, C, iter, ...)

Arguments

logpost	function definition of the log probability function
current	starting value of algorithm
C	half-width of proposal interval
iter	number of iterations
...	other inputs needed in logpost function

Value

S	vector of simulated values
accept_rate	acceptance rate of algorithm

Author(s)

Jim Albert

Examples

lpost <- function(theta, s){
  dnorm(s$ybar, theta, s$se, log = TRUE) +
    dcauchy(theta, s$loc, s$scale, log = TRUE)
}
s <- list(ybar = 20,
          se = 0.4,
          loc = 10,
          scale = 2)
post <- metropolis(lpost, 10, 20, 100, s)

bayesball/TeachBayes documentation built on Jan. 5, 2020, 1:47 a.m.