Full Seminar Archive

Our regular seminar program covers a broad range of topics from applied mathematics, pure mathematics and statistics. All staff and students are welcome. This page has a complete list of past seminars and a list restricted by year can be accessed via the left-hand menu.

Rob Salomone - UNSW Sydney
For sampling from a log-concave density, we study implicit integrators resulting from theta-method discretization of the overdamped Langevin diffusion stochastic differential equation. Theoretical...

A/Prof. Hanlin Shang - Australian National University
This paper is concerned with forecasting probability density functions. Density functions are nonnegative and have a constrained integral, and thus do not constitute a vector space. Implementation of...

Dr. Rachel Wang - University of Sydney
Variational approximation has been widely used in large-scale Bayesian inference recently, the simplest kind of which involves imposing a mean field assumption to approximate complicated latent...

Ian Renner - University of Newcastle
My research focuses on species distribution modelling, in which data collected on the presence of species is used to predict the distribution of species as a function of the environment. As simple as...

Benoit Liquet - Queensland University of Technology
It is well established that incorporation of prior knowledge on the structure existing in the data for potential grouping of the covariates is key to more accurate prediction and improved...

Michael Betancourt - Symplectomorphic, LLC
Despite the promise of big data, inferences are often limited not by sample size but rather by systematic effects.  Only by carefully modeling these effects can we take full advantage of the data --...

Imke Botha - Queensland University of Technology
Parameter inference for stochastic differential equation mixed effects models (SDEMEMs) is a challenging problem. Analytical solutions for these models are rarely available, which means thatthe...

Christopher Drovandi - Queensland University of Technology
Approximate Bayesian computation (ABC) is the standard method for performing Bayesian inference in the presence of a computationally intractable likelihood function. ABC proceeds by comparing the...

Tamara Broderick - Massachusetts Institute of Technology (MIT)
Bayesian methods are attractive for analyzing large-scale data due to in part to their coherent uncertainty quantification, ability to model complex phenomena, and ease of incorporating expert...

Dr Gery Geenens - UNSW Sydney
In this work, the defining properties of a valid measure of the dependence between two random variables are reviewed and complemented with two original ones, shown to be more fundamental than other...

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