Selected Publications

This paper introduces the parametric functional approximate Bayesian (PFAB) algorithm to accelerate computation for the intractable likelihood of the Potts model. We demonstrate this method using synthetic data as well as remotely-sensed imagery from the Landsat-8 satellite. We achieve up to a hundredfold improvement in the elapsed runtime, compared to the exchange algorithm or ABC. An open source implementation of our algorithm is available in the R package bayesImageS.
arXiv:1503.08066v3 [stat.CO], 2018

We introduce a sequential Monte Carlo (SMC) algorithm to separate an observed spectrum into a series of peaks plus a smoothly-varying baseline, corrupted by additive white noise. The peaks are modelled as Lorentzian, Gaussian, or pseudo-Voigt functions, while the baseline is estimated using a penalised cubic spline. An open source implementation of our algorithm is available in the R package serrsBayes.
arXiv:1604.07299v2 [stat.AP], 2018

Recent Publications

. Scalable Bayesian Inference for the Inverse Temperature of a Hidden Potts Model. arXiv:1503.08066v3 [stat.CO], 2018.

Preprint Code Project Slides Video

. Bayesian modelling and quantification of Raman spectroscopy. arXiv:1604.07299v2 [stat.AP], 2018.

Preprint Code Dataset Project Slides

. Pre-processing for approximate Bayesian computation in image analysis. Stat. Comput. 25(1): 23-33, 2015.

Preprint Code Project Source Document

Recent Posts

I’ve just arXived another revision of my paper on the PFAB algorithm: arXiv:1503.08066v3 [stat.CO]. It includes a rather elegant proof of the exact mean and variance of the sufficient statistic \(S(\mathbf{z})\) in the hottest state, when the inverse temperature \(\beta = 0\). The proof by my co-author Geoff Nicholls holds for any Potts model with first-order neighbours. That is, the nearest 4 neighbours in a 2D lattice (or 6 neighbours in 3D).




Bayesian algorithms for image segmentation.


Bayesian computation and inference for spectroscopy.

Research Supervision

I am available to supervise Honours, Masters, and PhD students in the School of Mathematics and Applied Statistics at Wollongong. If the above projects in spectroscopy and MCMC appeal to you, please get in touch with me via email.

Current Masters Students

Spring 2018 / Autumn 2019:

  • Lu Wang, “Experimental design for Multiplex Spectroscopy”

Past Masters Students

  • Jianyin Peng (MSc with distinction, University of Warwick, 2018) “A New Errors-in-variables Model and Bayesian Computation for Raman Spectra”


I am a lecturer for the following courses in the Spring 2018 session at Wollongong:

  • STAT101: Introduction to Statistics (coordinator: Dr Carole Birrell)
  • PSYC354: Design and Analysis (coordinator: Dr Sebastien Miellet)