7 papers
Analysing symbolic data by pseudo-marginal methods
Yu Yang, Matias Quiroz, Boris Beranger +2
Symbolic data analysis (SDA) aggregates large individual-level datasets into a small number of distributional summaries, such as random rectangles or random histograms. The inferen…
Predictive variational inference for flexible regression models
Lucas Kock, Scott A. Sisson, G. S. Rodrigues +1
A conventional Bayesian approach to prediction uses the posterior distribution to integrate out parameters in a density for unobserved data conditional on the observed data and par…
A correlated pseudo-marginal approach to doubly intractable problems
Yu Yang, Matias Quiroz, Robert Kohn +1
Doubly intractable models are encountered in a number of fields, e.g. social networks, ecology and epidemiology. Inference for such models requires the evaluation of a likelihood f…
Fast and flexible inference for spatial extremes
Peng Zhong, Scott A. Sisson, Boris Beranger
Statistical modelling of spatial extreme events has gained increasing attention over the last few decades with max-stable processes, and more recently -Pareto processes, becomin…
Positional Encoder Graph Quantile Neural Networks for Geographic Data
William E. R. de Amorim, Scott A. Sisson, T. Rodrigues +2
Positional Encoder Graph Neural Networks (PE-GNNs) are among the most effective models for learning from continuous spatial data. However, their predictive distributions are often…
Hidden Group Time Profiles: Heterogeneous Drawdown Behaviours in Retirement
Igor Balnozan, Denzil G. Fiebig, Anthony Asher +2
This article investigates retirement decumulation behaviours using the Grouped Fixed-Effects (GFE) estimator applied to Australian panel data on drawdowns from phased withdrawal re…