57 citations · 130 across the 11 of their papers we have counts for
11 papers
Dynamic Factor Analysis with Dependent Gaussian Processes for High-Dimensional Gene Expression Trajectories
Jiachen Cai, Robert J. B. Goudie, Colin Starr +1
The increasing availability of high-dimensional, longitudinal measures of gene expression can facilitate understanding of biological mechanisms, as required for precision medicine.…
Translating predictive distributions into informative priors
Andrew A. Manderson, Robert J. B. Goudie
When complex Bayesian models exhibit implausible behaviour, one solution is to assemble available information into an informative prior. Challenges arise as prior information is of…
A General Framework for Cutting Feedback within Modularised Bayesian Inference
Yang Liu, Robert J. B. Goudie
Standard Bayesian inference can build models that combine information from various sources, but this inference may not be reliable if components of a model are misspecified. Cut in…
Combining chains of Bayesian models with Markov melding
Andrew A. Manderson, Robert J. B. Goudie
A challenge for practitioners of Bayesian inference is specifying a model that incorporates multiple relevant, heterogeneous data sets. It may be easier to instead specify distinct…
Interoperability of statistical models in pandemic preparedness: principles and reality
George Nicholson, Marta Blangiardo, Mark Briers +12
We present "interoperability" as a guiding framework for statistical modelling to assist policy makers asking multiple questions using diverse datasets in the face of an evolving p…
Generalized Geographically Weighted Regression Model within a Modularized Bayesian Framework
Yang Liu, Robert J. B. Goudie
Geographically weighted regression (GWR) models handle geographical dependence through a spatially varying coefficient model and have been widely used in applied science, but its g…
A Bayesian framework for case-cohort Cox regression: application to dietary epidemiology
Andrew Yiu, Robert J. B. Goudie, Stephen J. Sharp +2
The case-cohort study design bypasses resource constraints by collecting certain expensive covariates for only a small subset of the full cohort. Weighted Cox regression is the mos…
Stochastic Approximation Cut Algorithm for Inference in Modularized Bayesian Models
Yang Liu, Robert J. B. Goudie
Bayesian modelling enables us to accommodate complex forms of data and make a comprehensive inference, but the effect of partial misspecification of the model is a concern. One app…
A numerically stable algorithm for integrating Bayesian models using Markov melding
Andrew A. Manderson, Robert J. B. Goudie
When statistical analyses consider multiple data sources, Markov melding provides a method for combining the source-specific Bayesian models. Markov melding joins together submodel…
MultiBUGS: A parallel implementation of the BUGS modelling framework for faster Bayesian inference
Robert J. B. Goudie, Rebecca M. Turner, Daniela De Angelis +1
MultiBUGS (https://www.multibugs.org) is a new version of the general-purpose Bayesian modelling software BUGS that implements a generic algorithm for parallelising Markov chain Mo…
Joining and splitting models with Markov melding
Robert J. B. Goudie, Anne M. Presanis, David Lunn +2
Analysing multiple evidence sources is often feasible only via a modular approach, with separate submodels specified for smaller components of the available evidence. Here we intro…