mcmcPottsNoData

Matt Moores

2018-06-06

This is a follow up to my previous post about the Swendsen-Wang (SW) algorithm, where I mentioned that SW has better convergence properties than Gibbs when the inverse temperature parameter \(\beta\) is large. This difference can be quantified by initialising the two algorithms at known starting points and measuring how many iterations it takes to converge. This is the second in a series of posts describing the functions and algorithms that I have implemented in the R package bayesImageS, which is now available on CRAN.


The Potts model has a doubly-intractable likelihood, so its expectation and variance cannot be computed exactly. Instead, we can use Markov chain Monte Carlo (MCMC) algorithms such as SW or Gibbs sampling to simulate from its distribution for a given value of \(\beta\). However, we need to know how many MCMC iterations to use, so that the chain will have converged to a steady state. Otherwise, any inference using the MCMC samples will be biased.

In the following, the labels z of the Potts model can take k different values. This state space is not ordered, so algorithms such as perfect sampling (Propp & Wilson, 1996; Huber, 2016) cannot be applied. The Potts model is a member of the exponential family, so it has a sufficient statistic S(z) which is the count of like neighbours. The maximum value of S(z), which we will call \(M\), is equal to \(2(n - \sqrt{n})\) for a regular, square lattice. For example, \(M = 112\) for an 8x8 lattice; \(M = 31,000\) for 125x125; and \(M = 1,998,000\) for 1000x1000.

There are two exceptions where the distribution of the Potts model can be computed exactly. When \(\beta=0\) the labels z are independent, hence the sufficient statistic S(z) follows a Binomial distribution with expectation \(M/k\) and variance \(M(1/k)(1 - 1/k)\). For an 8x8 lattice with k=3, the expectation is 64/3 with a variance of 24.89. As β approaches infinity, all of the labels have the same value almost surely. This means that the expectation approaches M asymptotically, while the variance approaches 0.

We can use the endpoints of the distribution to estimate how long the SW and Gibbs algorithms take to converge. The algorithm is initialised at one endpoint, then we monitor S(z) at each iteration until the distribution of the samples has converged to the known expectation and variance. First, let’s look at chequerboard Gibbs sampling for an 8×8 lattice with k=3:

k <- 3
n <- 8*8
mask <- matrix(1,nrow=sqrt(n),ncol=sqrt(n))
neigh <- getNeighbors(mask, c(2,2,0,0))
block <- getBlocks(mask, 2)
edges <- getEdges(mask, c(2,2,0,0))
print(paste(sum(mask),"pixels"))
## [1] "64 pixels"
print(paste("maximum sufficient statistic S(z) =",nrow(edges)))
## [1] "maximum sufficient statistic S(z) = 112"
res.Gibbs <- mcmcPottsNoData(beta=5, k=3, neigh, block, niter=100)
ts.plot(res.Gibbs$sum, ylim=c(nrow(edges)/3, nrow(edges)))
abline(h=nrow(edges), col=2, lty=3)
Chequerboard Gibbs sampling for a regular 8x8 lattice with k=3 labels.

Chequerboard Gibbs sampling for a regular 8x8 lattice with k=3 labels.

summary(res.Gibbs$sum[51:100])
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     112     112     112     112     112     112
var(res.Gibbs$sum[51:100])
## [1] 0

We can see that it only takes around 35 iterations for the Gibbs sampler to converge for a lattice of that size. Now for a 125×125 lattice:

n <- 125*125
mask <- matrix(1,nrow=sqrt(n),ncol=sqrt(n))
neigh <- getNeighbors(mask, c(2,2,0,0))
block <- getBlocks(mask, 2)
edges <- getEdges(mask, c(2,2,0,0))
print(paste(sum(mask),"pixels"))
## [1] "15625 pixels"
print(paste("maximum sufficient statistic S(z) =",nrow(edges)))
## [1] "maximum sufficient statistic S(z) = 31000"
res.Gibbs <- mcmcPottsNoData(beta=5, k=3, neigh, block, niter=2000)
ts.plot(res.Gibbs$sum, ylim=c(nrow(edges)/3, nrow(edges)))
abline(h=nrow(edges), col=2, lty=3)
Chequerboard Gibbs sampling for a regular 125x125 lattice with k=3 labels.

Chequerboard Gibbs sampling for a regular 125x125 lattice with k=3 labels.

summary(res.Gibbs$sum[1001:2000])
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   30642   30720   30768   30750   30787   30804
var(res.Gibbs$sum[1001:2000])
## [1] 2032.057

Even after 2000 iterations, the distribution of S(z) might not have converged to the known value. Now let’s see how Swendsen-Wang performs for the same lattice:

res.SW <- swNoData(beta=5, k=3, neigh, block, niter=50)
ts.plot(res.SW$sum, ylim=c(nrow(edges)/3, nrow(edges)))
abline(h=nrow(edges), col=2, lty=3)

summary(res.SW$sum[26:50])
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   31000   31000   31000   31000   31000   31000
var(res.SW$sum[26:50])
## [1] 0

After 25 iterations, SW has already converged to the exact distribution. Even though this algorithm is much more expensive for each iteration, it more than makes up for that in efficiency when \(\beta\) is large.

n <- 1000*1000
mask <- matrix(1,nrow=sqrt(n),ncol=sqrt(n))
neigh <- getNeighbors(mask, c(2,2,0,0))
block <- getBlocks(mask, 2)
edges <- getEdges(mask, c(2,2,0,0))
print(paste(sum(mask),"pixels"))
## [1] "1e+06 pixels"
print(paste("maximum sufficient statistic S(z) =",nrow(edges)))
## [1] "maximum sufficient statistic S(z) = 1998000"
res.SW <- swNoData(beta=5, k=3, neigh, block, niter=100)
ts.plot(res.SW$sum, ylim=c(nrow(edges)/3, nrow(edges)))
abline(h=nrow(edges), col=2, lty=3)

summary(res.SW$sum[81:100])
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
## 1998000 1998000 1998000 1998000 1998000 1998000
var(res.SW$sum[81:100])
## [1] 0

Now let’s see what happens when we go in the other direction: initialising the lattice with all labels set to the same value, then updating with \(\beta=0\):

n <- 125*125
mask <- matrix(1,nrow=sqrt(n),ncol=sqrt(n))
neigh <- getNeighbors(mask, c(2,2,0,0))
block <- getBlocks(mask, 2)
edges <- getEdges(mask, c(2,2,0,0))
system.time(res2.Gibbs <- mcmcPottsNoData(beta=0, k=3, neigh, block, niter=100, random=FALSE))
##    user  system elapsed 
##   0.165   0.002   0.168
ts.plot(res2.Gibbs$sum, ylim=range(c(res2.Gibbs$sum, nrow(edges))))
abline(h=nrow(edges), col=2, lty=3)
abline(h=nrow(edges)/3, col=4, lty=3)

summary(res2.Gibbs$sum[51:100])
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   10172   10261   10330   10331   10408   10544
var(res2.Gibbs$sum[51:100])
## [1] 7242.134

The distribution of all 500 samples are very close to the exact distribution with mean 10333.33 and variance 6888.89:

muSz <- nrow(edges)/3
sdSz <- sqrt(nrow(edges)*(1/3)*(2/3))
hist(res2.Gibbs$sum[51:100], freq=FALSE, breaks=20, col=3,
     xlim=range(c(res2.Gibbs$sum,muSz - 3*sdSz, muSz + 3*sdSz)))
abline(v=nrow(edges)/3, col=4, lty=3, lwd=3)
curve(dnorm(x, mean=nrow(edges)/3, sd=sqrt(nrow(edges)*(1/3)*(2/3))), 
          col="darkblue", lwd=2, lty=2, add=TRUE, yaxt="n")

Now for Swendsen-Wang:

system.time(res2.SW <- swNoData(beta=0, k=3, neigh, block, niter=100, random=FALSE))
##    user  system elapsed 
##   0.131   0.010   0.141
ts.plot(res2.SW$sum, ylim=range(c(res2.SW$sum, nrow(edges))))
abline(h=nrow(edges), col=2, lty=3)
abline(h=nrow(edges)/3, col=4, lty=3)

summary(res2.SW$sum[51:100])
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   10139   10294   10348   10347   10390   10552
var(res2.SW$sum[51:100])
## [1] 6509.104
hist(res2.SW$sum[51:100], freq=FALSE, breaks=20, col=3,
     xlim=range(c(res2.Gibbs$sum,muSz - 3*sdSz, muSz + 3*sdSz)))
abline(v=nrow(edges)/3, col=4, lty=3, lwd=3)
curve(dnorm(x, mean=muSz, sd=sdSz), 
          col="darkblue", lwd=2, lty=2, add=TRUE, yaxt="n")