# MCMC convergence

I'm trying to fit a Bayesian model. This model has many parameters (about 150).

I run MCMC (10000 iters) with a thinning period to avoid correlation problem and with the hope it would save me some time in terms of computation. So I obtain the sample that let me to compute Monte Carlo estimates. I knew that a proper inference implies checking for MCMC convergence. I perform this task with a graphic assessment (I know that there are more formal approaches, this is a first attempt) with trace plot. I realised that there are few parameters that doesn't show at all any convergence. What does It mean?

When I try to see what the effective sample size was, I obtain for these parameters very low values (like 6) and this is a sign of correlated samples.

I don't know how to go on.

UPDATE: This is my model in BUGS. it's a dirichlet regression, with response variable y made up with 4 categories( so K=4). i'm applying this to a dataset related to violent crime in state of usa usa. So my thought was to use a parameter beta for each observation i( the states in usa) Example of data:

     state  violent_crime    murder  rape robbery aggravated_assault   median_income
1   Alabama       20727    276  2005    4701              13745     42917
2   alaska        4684     41   771     629               3243      70898


Model:

model
{
for (i in 1:n) {
beta1[i,1:K]~dmnorm(zero[],t[1:K,1:K])

Y[i,1:K] ~ddirich(alpha[i,1:K])
for ( k in 1:4)
{
xb[i,k]<-exp(beta0[k]+beta1[i,k]*x[i])
}
for( k in 1:3)
{
mu[i,k]<-xb[i,k]/sum(xb[i,1:4])
}
mu[i,K]<-1/sum(xb[i,1:4])
for( k in 1:K)
{
alpha[i,k]<-mu[i,k]*phi
}

}
for(i in 1:K)
{
for (j in 1:K)
{  B[i,j] <- 0.00001*equals(i,j)
}}
#prior
beta0[1:K]~dmnorm(zero[],B[,])

for( k in 1:K)
{
zero[k] <- 0
}

#hyper-prior
t[1:K,1:K]~dwish(R[1:K,1:K],4)
phi~dgamma(0.0001,0.0001)

}

• What kind of sampler/software are you using? Mar 11, 2017 at 0:57
• I have written the model in openBugs and I m running it in R through R2OpenBugs package Mar 11, 2017 at 8:06
• Added some extra remarks to my (generic) answer. Hard to give specific advice without having more detailed information. Mar 11, 2017 at 13:18
• @MarcoFumagalli consider centering and/or standardizing your predictors. Lots of literature shows centering variables can help MCMC mixing. Mar 18, 2017 at 15:58

It means that your chain most likely did not converge. By this I mean you should be wary of the entire chain, not just worry about the dimensions with low effective number of samples.

### Solutions

Sometimes a low effective number of samples is just because the chain started in a low-probability region, and found the basin of convergence (the high probability region, or typical set) only later on. I do not recommend that now you just play with burn-in, since with only one chain it's hard to tell what's going on.

Instead, you might want to run a few chains in parallel, as opposed to a single long chain (see MacKay's book, Chapter 29). With multiple chains it is usually easier to spot lack of convergence, although there are different schools of thought here on the number of chains (I usually do 3 or 4).

Regarding burn-in, there are also several opinions. Some people, such as Geyer, say that it's pointless, as long as you are sure that you start in a high probability region:

Any point you don't mind having in a sample is a good starting point.

However, this is easier said than done in 150 dimensions. State-of-the-art statistical packages burn-in as much as 50% of the chain (see Stan), but one of the reasons for such a long burn-in is that it is also used to adapt some of the sampler parameters.

In your case, I definitely recommend that you initialize your sampler from a good guess (e.g., somewhere around the mode is better than nothing, although the mode itself is likely not in the typical set), and do some burn-in since in high dimensions it's hard to know where the probability mass resides (not the same as the probability density). In my opinion, it's better to burn-in more than less (at worst, you are simply throwing away some effective samples, whereas if you do not burn-in you might be keeping samples that are not representative of the target distribution).

Sample more

Simple enough, start where your MCMC chain ended (or from some other good guess, as mentioned above), discard the previous chain, and take more samples overall.