Let’s return to the example where we are sampling from a Cauchy density with unknown median theta and known scale parameter 1 and we place a uniform prior on theta. We observe the data

1.3 1.9 3.2 5.1 1.4 5.8 2.5 4.7 2.9 5.2 11.6 8.8 8.5 10.7 10.2

9.8 12.9 7.2 8.1 9.5

An attractive method of obtaining a simulated sample from this posterior is the Metropolis random walk algorithm. If theta^(t-1) represents the current simulated draw, then the next candidate is generated from the distribution

theta^(t) = theta^(t-1) + c Z,

where Z is standard normal. The main issue here is the selection of the scale constant c.

I did a simple experiment. I simulated samples of 10,000 draws using the scale values 0.2, 1, 5, 25. Here’s the R code for the Metropolis random walk with the choice of scale parameter 0.2.

cpost=function(theta,y)

{

val=0*theta

for (j in 1:length(y))

val=val+dt(y[j]-theta,df=1,log=TRUE)

return(val)

}

proposal=list(var=1,scale=.2)

fit1=rwmetrop(cpost,proposal,20,10000,data)

The acceptance rates for these four values are given by

scale acceptance rate

0.2 93%

1.0 73%

5.0 31%

25 7%

To assess the accuracy of a particular sample, we draw the exact posterior in red, and superimpose a density estimate of the simulated draws in blue.

Comparing these four figures, the scale values of 1 and 5 seem to do pretty well.