This chapter has been an informal introduction to Markov chain Monte Carlo (MCMC) estimation. The goal has been to introduce the purpose and approach MCMC algorithms. The major algorithms introduced were the Metropolis, Gibbs sampling, and Hamiltonian Monte Carlo algorithms. Each has its advantages and disadvantages. The ulam function in the rethinking package was introduced. It uses the Stan (mc-stan.org) Hamiltonian Monte Carlo engine to fit models as they are defined in this book. General advice about diagnosing poor MCMC fits was introduced by the use of a couple of pathological examples.
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9-1. Re-estimate the terrain ruggedness model from the chapter, but now using a uniform prior for the standard deviation, sigma. The uniform prior should be dunif(0,1). Visualize the priors. Use ulam to estimate the posterior. Visualize the posteriors for both models. Does the different prior have any detectible influence on the posterior distribution of sigma? Why or why not?
data(rugged) d <- rugged d$log_gdp <- log(d$rgdppc_2000) dd <- d[ complete.cases(d$rgdppc_2000) , ] dd$log_gdp_std <- dd$log_gdp/ mean(dd$log_gdp) dd$rugged_std<- dd$rugged/max(dd$rugged) dd$cid<-ifelse(dd$cont_africa==1,1,2) m8.3 <- quap( alist( log_gdp_std ~ dnorm( mu , sigma ) , mu <- a[cid] + b[cid]* (rugged_std-0.215) , a[cid] ~ dnorm(1,0.1), b[cid] ~ dnorm(0,0.3), sigma ~ dexp(1) ) , data=dd) precis(m8.3 , depth=2)
## mean sd 5.5% 94.5% ## a 0.8865643 0.015673916 0.86151434 0.9116142 ## a 1.0505670 0.009935462 1.03468822 1.0664458 ## b 0.1325045 0.074196328 0.01392442 0.2510845 ## b -0.1425776 0.054743232 -0.23006782 -0.0550873 ## sigma 0.1094814 0.005933570 0.09999836 0.1189643