#------------------------------------------------------------------------------ # Chapter 3: solutions to exercises # ----------------------------------------------------------------------------- # 1)--------------------------------------------------------- library(blmeco) triplot.normal.knownvariance(20, 3, 100, prior.theta=18, prior.variance=1) #------------------------------------------------------------- #2)----------------------------------------- --------------- library(arm) a <- c(10.3, 11.3, 9.9, 8.5, 9.7, 8.4, 10.1) b <- c(1.0, 17.6, 21.8, 14.1, 8.4, 12.0, 10.3, 13.0, 22.7) ma <- lm(a~1) # estimate mean and sd using LS mb <- lm(b~1) nsim <- 2000 bsima <- sim(ma, n.sim=nsim) # posterior distribution of mean and sd bsimb <- sim(mb, n.sim=nsim) ratioba <- bsimb@coef/bsima@coef hist(ratioba) quantile(ratioba, prob=c(0.025, 0.5, 0.975)) # estiamte with 95% Cri sum(ratioba>1.5)/nsim # posterior prob H: b/a>1.5