15 citations · 22 across the 9 of their papers we have counts for
9 papers
Habitual lifestyle timing explains circadian timing, but daily lifestyle changes do not, in free-living humans across 2000 days
Billy C. Smith, Zeel Pansara, Rosanne H. Timmerman +11
Background: Both between- and within-subject variations in circadian timing matter for health. If lifestyle changes could be used to regulate circadian timing, they would offer acc…
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…
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…
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…
A sparse Bayesian hierarchical vector autoregressive model for microbial dynamics in a wastewater treatment plant
Naomi E. Hannaford, Sarah E. Heaps, Tom M. W. Nye +4
Proper function of a wastewater treatment plant (WWTP) relies on maintaining a delicate balance between a multitude of competing microorganisms. Gaining a detailed understanding of…
Bayesian spatio-temporal model for high-resolution short-term forecasting of precipitation fields
Stephen Richard Johnson, Sarah Elizabeth Heaps, Kevin James Wilson +1
With extreme weather events becoming more common, the risk posed by surface water flooding is ever increasing. In this work we propose a model, and associated Bayesian inference sc…
Incorporating compositional heterogeneity into Lie Markov models for phylogenetic inference
Naomi E. Hannaford, Sarah E. Heaps, Tom M. W. Nye +2
Phylogenetics uses alignments of molecular sequence data to learn about evolutionary trees. Substitutions in sequences are modelled through a continuous-time Markov process, charac…
Enforcing stationarity through the prior in vector autoregressions
Sarah E. Heaps
Stationarity is a very common assumption in time series analysis. A vector autoregressive process is stationary if and only if the roots of its characteristic equation lie outside…
The effect of non-reversibility on inferring rooted phylogenies
S. Cherlin, T. M. W. Nye, S. E. Heaps +3
Most phylogenetic models assume that the evolutionary process is stationary and reversible. As a result, the root of the tree cannot be inferred as part of the analysis because the…