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Conference Season

I’ll be attending the final i-Like workshop in Newcastle-upon-Tyne, then heading to ABC in Edinburgh and the ISBA World Meeting before returning to Warwick for BAYSM (the Bayesian Young Statisticians’ Meeting). I’ll also be presenting a talk about bayesImageS at the UseR! conference in Brisbane. Titles and abstracts for my talks are below.

This will be my farewell tour of the UK, as I’ll be relocating back to Australia after an amazing four years as a postdoc at the University of Warwick. After UseR!, I’ll be taking up a lectureship in the School of Mathematics and Statistics and the National Institute for Applied Statistics Research Australia (NIASRA) at the University of Wollongong.

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CUDA in R on macOS X

The R package gputools has been consumed in the CRANpocalypse, but version 1.1 and earlier can still be downloaded as a source package from the archive. In order to compile it for macOS 10.12.6 (Sierra), you will need to install version 8 of the CUDA Toolkit as well as version 8.2.1 of the Xcode command-line tools. Even then, there are some major configuration issues that need to be dealt with. For the exceptionally brave, the excruciating details are below…

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R package serrsBayes

My second R package, serrsBayes, is now available on CRAN. serrsBayes uses a sequential Monte Carlo (SMC) algorithm to separate an observed spectrum into 3 components: the peaks s_i(\tilde\nu); baseline \xi_i(\tilde\nu); and additive white noise \epsilon_{i,j} \sim \mathcal{N}(0, \sigma^2_\epsilon):

\mathbf{y}_i = \xi_i(\tilde\nu) + s_i(\tilde\nu) + \boldsymbol\epsilon_i

More details about the model and SMC algorithm are available in my preprint on arXiv (Moores et al., 2006; v2 2018). The following gives an example of applying serrsBayes to surface-enhanced Raman spectroscopy (SERS) from a previous paper (Gracie et al., 2016).

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Chain reaction of CRAN death

If you want to destroy my sweater
Hold this thread as I walk away

Undone — Weezer

I received an unexpected email about the new version 0.5-0 of bayesImageS:

Dear maintainer,

Please see the problems shown on

Please correct before 2018-02-11 to safely retain your package on CRAN.

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Parametric Functional Approximate Bayesian (PFAB) algorithm

A new version 0.5-0 of my R package bayesImageS is now available on CRAN. To accompany it is a revision to my paper with Kerrie and Tony, “Scalable Bayesian inference for the inverse temperature of a hidden Potts model.” (Moores, Pettitt & Mengersen, arXiv:1503.08066v2). This paper introduces the parametric functional approximate Bayesian (PFAB) algorithm (the ‘p’ is silent…), which is a form of Bayesian indirect likelihood (BIL).

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This post looks at the convergence of the chequerboard Gibbs sampler for the hidden Potts model, in the presence of an external field. This algorithm is implemented as the function mcmcPotts in my R package, bayesImageS. Previous posts have looked at the convergence of Gibbs and Swendsen-Wang algorithms without an external field, as implemented in mcmcPottsNoData and swNoData functions.

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Coupling from the Past

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.

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