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End-of-year talks

December 3, 2018

A couple of seminars that I wanted to highlight in the next couple of weeks: one from A/Prof Mirko Draca (Department of Economics, University of Warwick) and another from me. Mirko will be speaking in the NIASRA Seminar Series this week at Wollongong, while I’ll be flying home to Brisbane next week to present a talk at the ACEMS Workshop on Intractable Likelihoods & ABC. Abstracts for both talks are below:

How Polarised are Citizens? Measuring Ideology from the Ground Up

When: 11am, Wednesday December 5

Where: Building 39A, Room 208, University of Wollongong, NSW (main campus)

Speaker: A/Prof Mirko Draca, Department of Economics, University of Warwick, UK


Strong evidence has been emerging that major democracies have become more politically polarized, at least according to measures based on the ideological positions of political elites. We ask: have the general public (‘citizens’) followed the same pattern? Our approach is based on unsupervised machine learning models as applied to issue- position survey data. This approach firstly indicates that coherent, latent ideologies are strongly apparent in the data, with a number of major, stable types that we label as: Liberal Centrist, Conservative Centrist, Left Anarchist and Right Anarchist. Using this framework, and a resulting measure of ‘citizen slant’, we are then able to decompose the shift in ideological positions across the population over time. Specifically, we find evidence of a ‘disappearing center’ in a range of countries with citizens shifting away from centrist ideologies into anti-establishment ‘anarchist’ ideologies over time. This trend is especially pronounced for the US.

This is joint work with Carlo Schwarz (University of Warwick)


The parametric functional approximate Bayesian algorithm for the Potts model

When: Wednesday December 12

Where: P504, QUT Gardens Point Campus, George St, Brisbane QLD

Speaker: Dr Matt Moores, Lecturer in Statistical Science, University of Wollongong


The hidden Potts model can be used for image segmentation, where the pixels are assumed to be noisy observations of some hidden states. The inverse temperature parameter governs the strength of spatial cohesion between neighbours in the image lattice. A difficulty arises from the dependence of an intractable normalising constant on the value of this parameter and thus there is no closed-form solution for sampling from the posterior distribution directly. There are a variety of computational approaches 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. 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’.

This is joint work with Kerrie Mengersen & Tony Pettitt (QUT) and Geoff Nicholls (Oxford).


From → MCMC

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