Gamma-Poisson MCMC
# STEP 1: DEFINE the model
gp_model <- "
data {
int<lower = 0> Y[2];
}
parameters {
real<lower = 0> lambda;
}
model {
Y ~ poisson(lambda);
lambda ~ gamma(3, 1);
}
"
# STEP 2: SIMULATE the posterior
gp_sim <- rstan::stan(model_code = gp_model, data = list(Y = c(2,8)),
chains = 4, iter = 5000*2, seed = 84735)
##
## SAMPLING FOR MODEL 'f8b2f6f1ba85f0a13853f4f36fd0d66c' NOW (CHAIN 1).
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# Trace plots of the 4 Markov chains
mcmc_trace(gp_sim, pars = "lambda", size = 0.1)
# Histogram of the Markov chain values
mcmc_hist(gp_sim, pars = "lambda") +
yaxis_text(TRUE) +
ylab("count")
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
# Density plot of the Markov chain values
mcmc_dens(gp_sim, pars = "lambda") +
yaxis_text(TRUE) +
ylab("density")
