In cboettig/nonparametric-bayes: Nonparametric Bayes inference for ecological models

Run the Bayesian analysis to obtain posterior distributions for parameters.

knit("allen.Rmd")

pardist <- mcmcall

Transform parameters back

pardist[,4] = exp(pardist[,4])
pardist[,5] = exp(pardist[,5])

Compute optimal policy

sdp = f_transition_matrix(f, p, x_grid, h_grid, sigma_g, pardist)
s_opt <- value_iteration(sdp, x_grid, h_grid, OptTime=1000, xT, profit, delta)

Compare to the case without parameter uncertainty (growth noise only)

SDP_Mat <- determine_SDP_matrix(f, p, x_grid, h_grid, sigma_g)
pars_fixed <- value_iteration(SDP_Mat, x_grid, h_grid, OptTime=1000, xT, profit, delta)

Plot results

require(reshape2)
policies <- melt(data.frame(stock=x_grid, pars.uncert = x_grid[s_opt$D], pars.fixed = x_grid[pars_fixed$D]), id="stock")
ggplot(policies, aes(stock, stock - value, color=variable)) + geom_line(alpha=1) + xlab("stock size") + ylab("escapement") 

cboettig/nonparametric-bayes documentation built on May 13, 2019, 2:09 p.m.