Search Results for author: Sylvia Richardson

Found 9 papers, 6 papers with code

Bayesian outcome-guided multi-view mixture models with applications in molecular precision medicine

no code implementations1 Mar 2023 Paul D. W. Kirk, Filippo Pagani, Sylvia Richardson

Clustering is commonly performed as an initial analysis step for uncovering structure in 'omics datasets, e. g. to discover molecular subtypes of disease.

Clustering

A spatio-temporal framework for modelling wastewater concentration during the COVID-19 pandemic

no code implementations Environment International 2023 Guangquan Li, Hubert Denise, Peter Diggle, Jasmine Grimsley, Chris Holmes, Daniel James, Radka Jersakova, Callum Mole, George Nicholson, Camila Rangel Smith, Sylvia Richardson, William Rowe, Barry Rowlingson, Fatemeh Torabi, Matthew J. Wade, Marta Blangiardo

Methods to detect the presence of SARS-CoV-2 RNA in wastewater were developed early in the pandemic, and extensive work has been conducted to evaluate the relationship between viral concentration and COVID-19 case numbers at the catchment areas of sewage treatment works (STWs) over time.

Epidemiology

Kernel learning approaches for summarising and combining posterior similarity matrices

1 code implementation27 Sep 2020 Alessandra Cabassi, Sylvia Richardson, Paul D. W. Kirk

Here we build upon the notion of the posterior similarity matrix (PSM) in order to suggest new approaches for summarising the output of MCMC algorithms for Bayesian clustering models.

Clustering Data Integration

Distributed Computation for Marginal Likelihood based Model Choice

no code implementations10 Oct 2019 Alexander Buchholz, Daniel Ahfock, Sylvia Richardson

We propose a general method for distributed Bayesian model choice, using the marginal likelihood, where a data set is split in non-overlapping subsets.

Shrinkage estimation of large covariance matrices using multiple shrinkage targets

2 code implementations21 Sep 2018 Harry Gray, Gwenaël G. R. Leday, Catalina A. Vallejos, Sylvia Richardson

Linear shrinkage estimators of a covariance matrix --- defined by a weighted average of the sample covariance matrix and a pre-specified shrinkage target matrix --- are popular when analysing high-throughput molecular data.

Methodology Applications

Fast Bayesian inference in large Gaussian graphical models

2 code implementations21 Mar 2018 Gwenaël G. R. Leday, Sylvia Richardson

Specifically, we introduce closed-form Bayes factors under the Gaussian conjugate model to evaluate the null hypotheses of marginal and conditional independence between variables.

Methodology

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