CS计算机代考程序代写 Code

Code

Code

Elena Moltchanova

2 Aug 2021

To evaluate the posterior probability

Pr(µTue > µMon|x, α0, β0) =
∫ +∞

0

∫ +∞
µMon

f(µTue|x, α0, β0)f(µMon|x, α0, β0)dµTuedµMon.

### 2 Aug 2021
# By EM
# evaluating posterior probability

rm(list=ls())

mu.mon <- rgamma(10^4, 750, 11) mu.tue <- rgamma(10^4, 765, 11) hist(mu.mon) ## Pr(mu.tue > mu.mon|data)

mean(mu.tue>mu.mon)

To evaluate the posterior predictive probability:

Pr(x̃ > 80|x, α0, β0)

### Pr(x.tilde > 80 | data)

# 1. simulate 10^4 mus from the post. dist
# 2. simulate 10^4 new x from the likelihood
mu.mon <- rgamma(10^4, 750, 11) x.tilde <- rpois(10^4,mu.mon) mean(x.tilde>80)

quantile(x.tilde,.99)

Which day-of-the-week has the highest average demand?
### which DoW has the highest demand
mu.wed <- rgamma(10^4, 772, 11) mu.thu <- rgamma(10^4, 763, 11) mu.fri <- rgamma(10^4, 770, 11) mu.table <- cbind(mu.mon,mu.tue,mu.wed,mu.thu,mu.fri) 1 DoW <- apply(mu.table,1,which.max) table(DoW)/length(DoW) ## ratio tue/mon ratio.t.m <- mu.tue/mu.mon mean(ratio.t.m) quantile(ratio.t.m,c(.025,.975)) 2