9 papers
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…
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…
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…
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…
Recursive variational Gaussian approximation with the Whittle likelihood for linear non-Gaussian state space models
Bao Anh Vu, David Gunawan, Andrew Zammit-Mangion
Parameter inference for linear and non-Gaussian state space models is challenging because the likelihood function contains an intractable integral over the latent state variables.…