Bayesian model choice for the Poisson model

Following Arthur Charpentier‘s example, I am going to try to post occasionally on material covered during my courses, in the hope that it might be useful to my students, but also to others.

In the second practical of the Bayesian Case Studies course, we looked at Bayesian model choice and basic Monte Carlo methods, looking at data about the number of oral questions asked by French deputies (Members of Parliament). We wanted to know whether male and female deputies behave differently in this matter. If we model the number of questions Y_i asked by a deputy by a Poisson random variable and denote Z_i their gender (1 for men, 2 for women), we thus need to choose between the following two models. (See the course website for the question sheet and other material; the slides are below.)

Slides2 from Robin Ryder

Model 1: Y_i\sim Poisson(\lambda); \quad \lambda \sim \Gamma(a,b)

Model 2: Y_i|Z_i=k\sim Poisson(\lambda_k); \quad \lambda_1\sim \Gamma(a,b); \quad \lambda_2\sim \Gamma(a,b)

First download the data and set some variables:

 > deputes=read.csv2('http://www.ceremade.dauphine.fr/~ryder/CaseStudies/deputes.csv')
 > attach(deputes)#Q1
 > summary(questions_orales)
 > hist(questions_orales,breaks=(0:6-.5))
 > (n=length(questions_orales))
 > (nh=sum(sexe=="H"))
 > (nf=sum(sexe=="F"))
 > (qtot=sum(questions_orales))
 > (qh=sum(questions_orales[sexe=="H"]))
 > (qf=sum(questions_orales[sexe=="F"]))

Since the Gamma prior is a conjugate prior for the Poisson model, we get a Gamma distribution as our posterior. Using a \Gamma(2,2) as prior, here is a comparison of the prior and the posteriors for the two models (in the bottom plot, \lambda_1 is in black and \lambda_2 in red.

Posterior

Two observations here: First, the posterior is much more peaked than the prior, which is expected since the number of data points is large. Second, in model 2, it seems that there may be a difference between men and women, with women asking slightly less questions in Parliament.To verify this, we compute a Bayes factor to compare the two models. In this case, the Bayes factor is available in closed form, but an initial computation does not work because n is too large:

> BF_analytical=function(a,b,y1,n1,y2,n2){
+   m1=b^a/gamma(a)*gamma(a+y1+y2)/(b+n1+n2)^(a+y1+y2)
+   m2=b^(2*a)/gamma(a)^2*gamma(a+y1)/(b+n1)^(a+y1)*gamma(a+y2)/(b+n2)^(a+y2)
+   return(m2/m1)
+ }
> BF_analytical(2,2,qh,nh,qf,nf)
[1] NaN
Warning messages:
1: In BF_analytical(2, 2, qh, nh, qf, nf) :
  value out of range in 'gammafn'
2: In BF_analytical(2, 2, qh, nh, qf, nf) :
  value out of range in 'gammafn'

However, we are able to compute the Bayes factor analytically by using the log-scale instead.

> BF_analytical2=function(a,b,y1,n1,y2,n2){
+   m1=a*log(b)-lgamma(a)+lgamma(a+y1+y2)-(a+y1+y2)*log(b+n1+n2)
+   m2=2*a*log(b)-2*lgamma(a)+lgamma(a+y1)-(a+y1)*log(b+n1)+lgamma(a+y2)-(a+y2)*log(b+n2)
+   return(exp(m2-m1))
+ }
> BF_analytical2(2,2,qh,nh,qf,nf)
[1] 0.1875569
> log10(BF_analytical2(2,2,qh,nh,qf,nf))
[1] -0.726867

Using Jeffrey’s scale of evidence, this corresponds to “substantial” evidence in favour of model 1 (i.e. no difference between men and women).

Even if we had not been able to calculate the Bayes factor exactly, we could have approximated it using Monte Carlo. We first create two functions to calculate the likelihood under model 1 and model 2, and start with vanilla Monte Carlo.

> lkd.model1=function(y,n,lambda){  return(exp(-n*lambda+y*log(lambda))) }
> lkd.model2=function(y1,n1,y2,n2,lambda1,lambda2){  return(exp(-n1*lambda1+y1*log(lambda1)-n2*lambda2+y2*log(lambda2))) }

> BF_MC=function(a,b,y1,n1,y2,n2,M){
 +  lambda1=rgamma(M,a,b)
 +  m1=cumsum(lkd.model1(y1+y2,n1+n2,lambda1))/(1:M)
 + lambda2.1=rgamma(M,a,b)
 + lambda2.2=rgamma(M,a,b)
 +  m2=cumsum(lkd.model2(y1,n1,y2,n2,lambda2.1,lambda2.2))/(1:M)
 +  return(m2/m1)
 + }

> M=100000
> BF_MC(2,2,qh,nh,qf,nf,M)[M]
[1] 0.1776931
> plot(100:M,BF_MC(2,2,qh,nh,qf,nf,M)[100:M], type="l")

The last line creates a plot of how our Monte Carlo estimate of the Bayes factor changes as the number of iterations M increases. It is useful to check convergence.vanilla

Here, it is clear that the method converges after a few tens of thousands iterations. Our estimate for M=10^5 is close to the true value (two decimal places precision). Next, we try the harmonic mean method:

> BF_HM=function(a,b,y1,n1,y2,n2,M){
+   lambda1=rgamma(M,a+y1+y2,b+n1+n2)
+   m1=(1:M)/(cumsum(1/lkd.model1(y1+y2,n1+n2,lambda1)))
 +  lambda2.1=rgamma(M,a+y1,b+n1)
 +  lambda2.2=rgamma(M,a+y2,b+n2)
 +  m2=(1:M)/cumsum(1/lkd.model2(y1,n1,y2,n2,lambda2.1,lambda2.2))
+   return(m2/m1)  
+ }

> BF_HM(2,2,qh,nh,qf,nf,M)[M]
[1] 0.2451013
> plot(100:M,BF_HM(2,2,qh,nh,qf,nf,M)[100:M], type="l")
harmonic

We observe that this does not appear to converge. It is known that in general, the harmonic mean method is unreliable.

We finally move on to importance sampling. In importance sampling, we want to estimate E[h(X)] where X\sim f by using simulations from an alternate density g. This will be most efficient when g is close to $h\times f$. In this case, we therefore need g to be similar to the posterior distribution, so we take a normal density with mean and variance the same as the posterior distribution:

> BF_IS=function(a,b,y1,n1,y2,n2,M){
+  mean1=(a+y1+y2)/(b+n1+n2)
+  mean2.1=(a+y1)/(b+n1)
+  mean2.2=(a+y2)/(b+n2)

+  sigma1=sqrt((a+y1+y2)/(b+n1+n2)^2)
+  sigma2.1=sqrt((a+y1)/(b+n1)^2)
+  sigma2.2=sqrt((a+y2)/(b+n2)^2)

+  lambda1=rnorm(M,mean1,sigma1)
+  m1=cumsum( lkd.model1(y1+y2,n1+n2,lambda1) * dgamma(lambda1,a, b) / dnorm(lambda1,mean1,sigma1))/(1:M)

+  lambda2.1=rnorm(M,mean2.1, sigma2.1)
+  lambda2.2=rnorm(M,mean2.2, sigma2.2)
+  m2=cumsum( lkd.model2(y1,n1,y2,n2,lambda2.1,lambda2.2) * dgamma(lambda2.1,a, b) * dgamma(lambda2.2,a,b) / 
+    (dnorm(lambda2.1,mean2.1,sigma2.1)*dnorm(lambda2.2,mean2.2,sigma2.2))) / (1:M)

+  return(m2/m1)
+ }

> BF_IS(2,2,qh,nh,qf,nf,M)[M]
[1] 0.1875875
> plot(100:M,BF_IS(2,2,qh,nh,qf,nf,M)[100:M], type="l")

ISThis method converges. Note that the y-scale is much tighter than for vanilla Monte Carlo: convergence is in fact much faster, and we get a more precise estimate than by using vanilla Monte Carlo. This is more obvious if we look at both methods on the same graph.

> plot(100:M,BF_MC(2,2,qh,nh,qf,nf,M)[100:M], type="l") 
> lines(100:M,BF_IS(2,2,qh,nh,qf,nf,M)[100:M], col="red")
comparison

It is much more visible now that importance sampling is a clear winner.

With this computation of the Bayes factor, we choose model 1: a single parameter can be used to model the behaviour of men and women.

About these ads

Tags:

3 Responses to “Bayesian model choice for the Poisson model”

  1. Enseigner, et bloguer | Freakonometrics Says:

    [...] quand tu auras le temps” (Robin évoque ce point pour introduire son dernier billet, sur  http://robinryder.wordpress.com). Comme je n’ai publié aucun billet répondant à la question, je me [...]

  2. Blaquans Says:

    I think that this kind of post could also be added to the R Programming wikibooks.

    I’ve been developping the R Programming wikibooks alone for months. I would be happy to work with some new contributors.

    http://en.wikibooks.org/wiki/R_Programming

  3. Johne459 Says:

    Great, thanks for sharing this article. Really Cool. egafadcbefce

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s


Follow

Get every new post delivered to your Inbox.

%d bloggers like this: