110 citations · 461 across the 50 of their papers we have counts for
9 papers · 1 filter
Being Bayesian in the 2020s: opportunities and challenges in the practice of modern applied Bayesian statistics
Joshua J. Bon, Adam Bretherton, Katie Buchhorn +12
Building on a strong foundation of philosophy, theory, methods and computation over the past three decades, Bayesian approaches are now an integral part of the toolkit for most sta…
Many-levelled continuation ratio models for frequency of alcohol and drug use data
Mark Chambers, Christopher Drovandi
Studies of alcohol and drug use are often interested in the number of days that people use the substance of interest over an interval, such as 28 days before a survey date. Althoug…
The effect of biologically mediated decay rates on modelling soil carbon sequestration in agricultural settings
Mohammad Javad Davoudabadi, Daniel Pagendam, Christopher Drovandi +2
Microbial biomass carbon (MBC), a crucial soil labile carbon fraction, is the most active component of the soil organic carbon (SOC) that regulates bio-geochemical processes in ter…
Bayesian Detectability of Induced Polarisation in Airborne Electromagnetic Data using Reversible Jump Sequential Monte Carlo
Laurence Davies, Alan Yusen Ley-Cooper, Matthew Sutton +1
Detection of induced polarisation (IP) effects in airborne electromagnetic (AEM) measurements does not yet have an established methodology. This contribution develops a Bayesian ap…
Estimating a novel stochastic model for within-field disease dynamics of banana bunchy top virus via approximate Bayesian computation
Abhishek Varghese, Christopher Drovandi, Kerrie Mengersen +1
The Banana Bunchy Top Virus (BBTV) is one of the most economically important vector-borne banana diseases throughout the Asia-Pacific Basin and presents a significant challenge to…
Sequential Experimental Design for Predator-Prey Functional Response Experiments
Hayden Moffat, Markus Hainy, Nikos E. Papanikolaou +1
Understanding functional response within a predator-prey dynamic is a cornerstone for many quantitative ecological studies. Over the past 60 years, the methodology for modelling fu…
Efficient Bayesian estimation for GARCH-type models via Sequential Monte Carlo
Dan Li, Adam Clements, Christopher Drovandi
The advantages of sequential Monte Carlo (SMC) are exploited to develop parameter estimation and model selection methods for GARCH (Generalized AutoRegressive Conditional Heteroske…
ABC model selection for spatial extremes models applied to South Australian maximum temperature data
Xing Ju Lee, Markus Hainy, James P. McKeone +2
Max-stable processes are a common choice for modelling spatial extreme data as they arise naturally as the infinite-dimensional generalisation of multivariate extreme value theory.…
Using history matching for prior choice
Xueou Wang, David J. Nott, C. C. Drovandi +2
It can be important in Bayesian analyses of complex models to construct informative prior distributions which reflect knowledge external to the data at hand. Nevertheless, how much…