Metropolis-Hastings algorithm random-walk candidate density

In Section 6.1 we show how the Metropolis-Hastings algorithm can be used to find a Markov chain that has the posterior as its long-run distribution in the case of a single parameter. There are two kinds of candidate densities we can use, random-walk candidate densities, or independent candidate densities. We see how the chain... 

A correlated bivariate normal distribution. The function blvnormMH can use the Metropolis-Hastings algorithm to draw a sample from a correlated bivariate normal target density using either an independent candidate density or a random-walk candidate density when we are drawing both parameters in a single draw. Also,... 

A mixture of two normal distributions. The macros NormMixMHRW. mac and NormMixMHInd.mac use the Metropolis-Hastings algorithm to draw a sample from a univariate target distribution that is a mixture of two normal distributions using an independent Normal candidate density and a random-walk normal candidate density, respectively. Table A.6 shows the Minitab commands to set up and run these two macros. 

---