How To Generate Any Probability Distribution, Part 2: The Metropolis-Hastings Algorithm

In an earlier post I discussed how to use inverse transform sampling to generate a sequence of random numbers following an arbitrary, known probability distribution. In a nutshell, it involves drawing a number x from the uniform distribution between 0 and 1, and returning CDF-1(x), where CDF is the cumulative distribution function corresponding to the probability …