#### Filter Results:

- Full text PDF available (9)

#### Publication Year

1995

2015

- This year (0)
- Last 5 years (5)
- Last 10 years (8)

#### Publication Type

#### Co-author

#### Journals and Conferences

#### Key Phrases

#### Method

#### Organism

Learn More

Statistical problems where ‘the number of things you don’t know is one of the things you don’t know’ are ubiquitous in statistical modelling. They arise both in traditional modelling situations such as variable selection in regression, and in more novel methodologies such as object recognition, signal processing, and Bayesian nonparametrics. All such… (More)

- David Hastie
- 2005

Since its introduction by Green (1995), reversible jump MCMC has been recognised as a powerful tool for making posterior inference about a wide range of statistical problems. Despite enjoying considerable application across a variety of disciplines, the method’s popularity has been tempered by the common perception that reversible jump samplers can be… (More)

We review the across-model simulation approach to computation for Bayesian model determination, based on the reversible jump Markov chain Monte Carlo method. Advantages, difficulties and variations of the methods are discussed. We also discuss some limitations of the ideal Bayesian view of the model determination problem, for which no computational methods… (More)

- Leonardo Bottolo, Marc Chadeau-Hyam, +24 authors Sylvia Richardson
- PLoS genetics
- 2013

Genome-wide association studies (GWAS) yielded significant advances in defining the genetic architecture of complex traits and disease. Still, a major hurdle of GWAS is narrowing down multiple genetic associations to a few causal variants for functional studies. This becomes critical in multi-phenotype GWAS where detection and interpretability of complex… (More)

- John Molitor, Jason G Su, +5 authors Michael Jerrett
- Environmental science & technology
- 2011

Recently, concerns have centered on how to expand knowledge on the limited science related to the cumulative impact of multiple air pollution exposures and the potential vulnerability of poor communities to their toxic effects. The highly intercorrelated nature of exposures makes application of standard regression-based methods to these questions… (More)

- Silvia Liverani, David I Hastie, Lamiae Azizi, Michail Papathomas, Sylvia Richardson
- Journal of statistical software
- 2015

PReMiuM is a recently developed R package for Bayesian clustering using a Dirichlet process mixture model. This model is an alternative to regression models, non-parametrically linking a response vector to covariate data through cluster membership (Molitor, Papathomas, Jerrett, and Richardson 2010). The package allows binary, categorical, count and… (More)

- Leonardo Bottolo, Marc Chadeau-Hyam, +5 authors Sylvia Richardson
- Bioinformatics
- 2011

SUMMARY
ESS++ is a C++ implementation of a fully Bayesian variable selection approach for single and multiple response linear regression. ESS++ works well both when the number of observations is larger than the number of predictors and in the 'large p, small n' case. In the current version, ESS++ can handle several hundred observations, thousands of… (More)

- David I. Hastie, Silvia Liverani, Sylvia Richardson
- Statistics and Computing
- 2015

We consider the question of Markov chain Monte Carlo sampling from a general stick-breaking Dirichlet process mixture model, with concentration parameter [Formula: see text]. This paper introduces a Gibbs sampling algorithm that combines the slice sampling approach of Walker (Communications in Statistics - Simulation and Computation 36:45-54, 2007) and the… (More)

Tumour multiplicity is a frequently measured phenotype in animal studies of cancer biology. Poisson variation of this measurement represents a biological and statistical reference point that is usually violated, even in highly controlled experiments, owing to sources of variation in the stochastic process of tumour formation. A recent experiment on murine… (More)

- Michail Papathomas, John Molitor, Clive Hoggart, David Hastie, Sylvia Richardson
- Genetic epidemiology
- 2012

We construct data exploration tools for recognizing important covariate patterns associated with a phenotype, with particular focus on searching for association with gene-gene patterns. To this end, we propose a new variable selection procedure that employs latent selection weights and compare it to an alternative formulation. The selection procedures are… (More)