Skip to content

International Workshop on Monte Carlo Methods for Spatial Stochastic Systems

June 24, 2015

I will be presenting a talk at the ACEMS International Workshop on Monte Carlo Methods for Spatial Stochastic Systems (MCMSS) at the University of Queensland, Brisbane, July 21-23 (abstract below). Other speakers include Gareth Roberts, Adrian Baddeley, Robert Kohn & Kevin Burrage. The workshop programme is now available online.

I’ll also be giving a practice talk at the Warwick Young Researchers’ Meeting (YRM) on June 30 and presenting an invited talk for the QUT Mathematical Sciences School on August 7.

Scalable Inference for the Inverse Temperature of a Hidden Potts Model
The Potts model is a discrete Markov random field that can be used to label the pixels in an image according to an unobserved classification. The strength of spatial dependence between neighbouring labels is governed by the inverse temperature parameter. This parameter is difficult to estimate, due to its dependence on an intractable normalising constant. Several approaches have been proposed, including the exchange algorithm and approximate Bayesian computation (ABC), but these algorithms do not scale well for images with a million or more pixels. We introduce a precomputed binding function, which improves the elapsed runtime of these algorithms by two orders of magnitude. Our method enables fast, approximate Bayesian inference for computed tomography (CT) scans and satellite imagery.

This is joint work with Kerrie Mengersen, Tony Pettitt and Chris Drovandi at QUT, and Christian Robert at the University of Warwick and Université Paris Dauphine:

Moores, Pettitt & Mengersen (2015) “Scalable Bayesian Inference for the Inverse Temperature of a Hidden Potts Model” arXiv:1503.08066 [stat.CO]

Moores, Drovandi, Mengersen & Robert (2015) “Pre-processing for approximate Bayesian computation in image analysis”
Statistics & Computing
 25(1): 23-33. DOI: 10.1007/s11222-014-9525-6

Advertisements

From → MCMC

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

Let's Look at the Figures

David Firth's blog

Nicholas Tierney

Computational Bayesian statistics

One weiRd tip

Computational Bayesian statistics

Series B'log

discussion blog for JRSS Series B papers

Mad (Data) Scientist

Musings, useful code etc. on R and data science

R-bloggers

R news and tutorials contributed by (750) R bloggers

Another Astrostatistics Blog

The random musings of a reformed astronomer ...

Darren Wilkinson's research blog

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

CHANCE

Computational Bayesian statistics

StatsLife - Significance magazine

Computational Bayesian statistics

(badness 10000)

Computational Bayesian statistics

Igor's Blog

Computational Bayesian statistics

Statisfaction

I can't get no

Xi'an's Og

an attempt at bloggin, nothing more...

Sam Clifford

Postdoctoral Fellow, Bayesian Statistics, Aerosol Science

Bayesian Research & Applications Group

Frontier Research in Bayesian Methodology & Computation

%d bloggers like this: