14 citations · 35 across the 35 of their papers we have counts for
40 papers
GeoMix: A Bayesian Framework for Stratigraphic and Geotechnical Inference
Bradley Wakefield, Michael Bertolacci, David Gunawan +3
Accurate characterisation of subsea sediments is essential for engineering projects such as offshore wind farms, but the challenging environment and large spatial domains involved…
Flexible Bayesian Models for Time-Varying Income Distributions
David Gunawan
Survey data are widely used to study how income inequality, poverty, and welfare evolve over time. A common practice is to estimate the income distribution separately for each year…
Neural Inference Functions for Margins for Time Series Copula Models
Daniel Fynn, David Gunawan, Andrew Zammit-Mangion
Copula models are widely employed in multivariate time series analysis because they permit flexible modelling of marginal distributions independently of the dependence structure, w…
Neural posterior inference with state-space models for calibrating ice sheet simulators
Bao Anh Vu, Andrew Zammit-Mangion, David Gunawan +2
Ice sheet models are routinely used to quantify and project an ice sheet's contribution to sea level rise. In order for an ice sheet model to generate realistic projections, its pa…
Bayesian copula-based spatial random effects models for inference with complex spatial data
Alan Pearse, David Gunawan, Noel Cressie
In this article, we develop fully Bayesian, copula-based, spatial-statistical models for large, noisy, incomplete, and non-Gaussian spatial data. Our approach includes novel constr…
Bayesian Inference for Non-Gaussian Simultaneous Autoregressive Models with Missing Data
Anjana Wijayawardhana, David Gunawan, Thomas Suesse
Standard simultaneous autoregressive (SAR) models typically assume normally distributed errors, an assumption often violated in real-world datasets that frequently exhibit non-norm…