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Canberra talk and other news

June 21, 2019

I’ll be giving a talk at ANU on Tuesday, for the Canberra branch of the Statistical Society of Australia: “Statistics from Mars – Bayesian signal processing for Raman Spectroscopy.” I’ll also be presenting a talk at the 12th International Conference on Monte Carlo Methods and Applications (MCM 2019) at UTS. I’ve volunteered as the newsletter editor for the Bayes Section of SSA. Our June newsletter is now available online.

Statistics from Mars: Bayesian signal processing for Raman spectroscopy

Tuesday, June 25 at 5:15 for a 6pm start

College of Business and Economics, Australian National University, Canberra, ACT

The planned Mars 2020 mission to Jezero Crater will include a rover equipped with 2 Raman spectrometers: SuperCam and SHERLOC. This would be the first time that this type of spectroscopy has been performed on the Martian surface, which will enable new kinds of analysis of minerals and organic molecules. In the meantime, the Mars Science Laboratory Curiosity rover continues to build on the massive dataset of laser-induced breakdown spectroscopy (LIBS) that it has been accumulating since 2012. These data pose particular challenges for statistical signal processing, since pre-flight calibration on Earth can only approximate Martian environmental conditions. Analytical methods must be robust to artefacts and other changes in the spectral profile, such as nonlinear interactions between signals. This talk will introduce a Bayesian method for source separation of spectroscopy. We derive informative priors from online databases of known reference spectra, as well as quantum-mechanical computer models. The components of the combined spectrum are identified and quantified using a sequential Monte Carlo algorithm. An open-source implementation of our method is available in the R package ‘serrsBayes.’

Bayesian Indirect Likelihood for the Potts Model

Tuesday, July 9 at 12pm

Business School, University of Technology, Sydney, NSW

The Potts model is commonly used for classification, where the labels are spatially-correlated. The strength of spatial association is governed by a smoothing parameter, known as the inverse temperature. A difficulty arises from the dependence of an intractable normalising constant on the value of this parameter, thus there is no closed-form solution for sampling from the posterior distribution directly. There are a variety of Markov chain Monte Carlo methods for sampling from the posterior without evaluating the normalising constant, including the exchange algorithm and approximate Bayesian computation (ABC). A serious drawback of these algorithms is that they do not scale well for models with a large state space, such as images with a million or more pixels. In this talk, I will introduce the parametric functional approximate Bayesian (PFAB) algorithm, which uses an integral curve to approximate the score function of the Potts model. PFAB incorporates known properties of the likelihood, such as heteroskedasticity and critical temperature. I will demonstrate this method using synthetic data as well as remotely-sensed imagery from the Landsat-8 satellite. The proposed algorithm achieves up to a hundredfold improvement in the elapsed runtime, compared to the exchange algorithm or ABC. An open source implementation of PFAB is available in the R package ‘bayesImageS.’

News from the Bayes Section of SSA

We are pleased to announce two upcoming talks by Dr Anthony Lee (Senior Lecturer from the University of Bristol): Tuesday, July 2 at QUT and Thursday, July 18 at Monash University. The call for abstracts has now opened for Bayes on the Beach. 250 word abstracts can be submitted by email to before August 16. We also mention some other upcoming conferences: MCM 2019, July 8-12 in Sydney;EAC-ISBA 2019, July 13-14 in Kobe, Japan; BayesComp 2020, January 7-10 in Florida, USA; and ABC in Grenoble, March 19-20 in France. Read more here

From → Functional Data, R

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