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stat.ME2026
Bayesian inference of sparsity in stable vector autoregressive processes
Sarah E. Heaps, Ian H. Jermyn, Yujiang Wang +1
Advances in sensing technology have made it possible to collect large volumes of high-dimensional time-series data. In fields like genetics and neuroscience, key questions concern…
stat.ME2024
Bayesian inference on the order of stationary vector autoregressions
Rachel L. Binks, Sarah E. Heaps, Mariella Panagiotopoulou +2
Vector autoregressions (VARs) are a widely used tool for modelling multivariate time-series. It is common to assume a VAR is stationary; this can be enforced by imposing the statio…
stat.ME2024
Structured prior distributions for the covariance matrix in latent factor models
Sarah Elizabeth Heaps, Ian Hyla Jermyn
Factor models are widely used for dimension reduction in the analysis of multivariate data. This is achieved through decomposition of a p x p covariance matrix into the sum of two…