# recycling accept-reject rejections (#2)

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**F**ollowing yesterday’s post on Rao’s, Liu’s, and Dunson’s paper on a new approach to intractable normalising constants, and taking advantage of being in Warwick, I tested the method on a toy model, namely the posterior associated with n Student’s t observations with unknown location parameter μ and a flat prior,

which is “naturally” bounded by a Cauchy density with scale √ν. The constant M is then easily derived and running the new algorithm follows from a normal random walk proposal targeting the augmented likelihood (R code below).

**A**s shown by the above graph, the completion-by-rejection scheme produces a similar outcome (tomato) as the one based on the sole observations (steelblue). With a similar acceptance rate. However, the computing time is much much degraded:

> system.time(g8()) user system elapsed 53.751 0.056 54.103 > system.time(g9()) user system elapsed 1.156 0.000 1.161

when compared with the no-completion version. Here is the entire R code that produced both MCMC samples:

#Student t observations and flat prior nu=4 n=25 M=pi*sqrt(nu) sqrtnu=sqrt(nu) obs=rt(n,df=4) sdobs=sd(obs) #unormalised t mydt=function(x,mu){ return(dt(x-mu,df=nu)/dt(0,df=nu))} mydtc=cmpfun(mydt) mydcauchy=function(x,mu){ y=(x-mu)/sqrtnu return(dcauchy(y)/sqrtnu)} mydcaucchy=cmpfun(mydcauchy) #augmented data augmen=function(mu){ y=NULL for (i in 1:n){ prop=mu+rcauchy(1)*sqrtnu reject=(runif(1)<mydtc(prop,mu)/(M*mydcaucchy(prop,mu))) while (!reject){ y=c(y,prop) prop=mu+rcauchy(1)*sqrtnu reject=(runif(1)<mydtc(prop,mu)/(M*mydcaucchy(prop,mu)))} } return(y)} #Gibbs gibbsda=function(T=10^4){ theta=rep(0,T) for (t in 2:T){ rej=augmen(theta[t-1]) theta[t]=prop=theta[t-1]+rnorm(1,sd=.1*sdobs) propdens=sum(dt(obs-prop,df=nu,log=TRUE))+ sum(log(mydcaucchy(rej,prop)-mydtc(rej,mu=prop)/M)) refdens=sum(dt(obs-theta[t-1],df=nu,log=TRUE))+ sum(log(mydcaucchy(rej,theta[t-1])-mydtc(rej,mu=theta[t-1])/M)) if (log(runif(1))>propdens-refdens) theta[t]=theta[t-1] } return(theta)} g8=cmpfun(gibbsda) gibbs2=function(T=10^4){ eta=rep(0,T) for (t in 2:T){ eta[t]=prop=eta[t-1]+rnorm(1,sd=sdobs) propdens=sum(dt(obs-prop,df=nu,log=TRUE)) refdens=sum(dt(obs-eta[t-1],df=nu,log=TRUE)) if (log(runif(1))>propdens-refdens) eta[t]=eta[t-1] } return(eta)} g9=cmpfun(gibbsda)

Filed under: R, Statistics, University life Tagged: accept-reject algorithm, compiler, Data augmentation, Gibbs sampling, MCMC, Monte Carlo Statistical Methods, Student’s t distribution

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