Skip to content

Coupling from the Past

November 1, 2017

In the stats department at Warwick we have a reading group who are currently discussing Mark Huber‘s 2015 book, Perfect Simulation. Back in May, I presented the 3rd chapter on Coupling from the Past (CFTP; Propp & Wilson, 1996). The mono_cftp_Ising function below implements monotonic CFTP for the Ising model (equivalent to the Potts model with only q=2 states). This algorithm returns a single, unbiased sample from the Ising model for a given inverse temperature, β. When combined with the exchange algorithm (Murray, Ghahramani & MacKay, 2006), this enables exact posterior inference for β. However, problems can occur when the value of β is too large, since the underlying single-site Gibbs sampler can fail to converge.

Previously, I’ve compared the Gibbs sampler with Swendsen-Wang for the Potts model, as implemented in my R package bayesImageS. I showed that the Gibbs sampler exhibits torpid mixing when β is larger than the critical value, \beta_{crit}. This slowdown can be quantified using CFTP, since it provides an accurate estimate of how many iterations an MCMC algorithm takes to converge.

Below the critical point, runtime is less than a second for 25,200 iterations of random scan, single-site Gibbs updates (T=5 recursions). The coalescence time roughly doubles with every increase in β (log-linear). At β=0.88, the average runtime is around 6 seconds for between 100k and 800k iterations. This trend accelerates beyond the critical point, requiring almost an hour for up to 838 million iterations to converge. This is for an image with only n=400 pixels. This torpid mixing can play havoc with the exchange algorithm, as you can imagine. For example, see the numerical results reported by McGrory, Titterington, Reeves & Pettitt in their 2009 paper.

CFTP might not be all that useful in practice, but it forms the basis for more advanced algorithms such as the perfect slice sampler of Mira, Møller & Roberts (2001) or the bounding chain for Swendsen-Wang (Huber, 2003). My code for the perfect slice sampler in the gist below appears to have a bug, but mono_cftp_Ising should be working fine.

From → MCMC, R

Leave a Comment

Leave a Reply

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

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

Google photo

You are commenting using your Google 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 )

Connecting to %s

Ella Kaye on Ella Kaye

Computational Bayesian statistics

Bayes' Food Cake

A bit of statistics, a bit of cakes. - Blogs to Learn R from the Community

Computational Bayesian statistics

Richard Everitt's blog

Computational Bayesian statistics

Let's Look at the Figures

David Firth's blog

Nicholas Tierney

Computational Bayesian statistics

Sweet Tea, Science

Two southern scientistas will be bringing you all that is awesome in STEM as we complete our PhDs. Ecology, statistics, sass.

Mad (Data) Scientist

Musings, useful code etc. on R and data science

Darren Wilkinson's blog

Statistics, computing, functional programming, data science, Bayes, stochastic modelling, systems biology and bioinformatics

(badness 10000)

Computational Bayesian statistics

Igor Kromin

Computational Bayesian statistics


I can't get no

Xi'an's Og

an attempt at bloggin, nothing more...

Sam Clifford

Postdoctoral Fellow, Bayesian Statistics, Aerosol Science

%d bloggers like this: