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07/30/2023
Bayesian dependent mixture models: A predictive comparison and survey
For exchangeable data, mixture models are an extremely useful tool for d...
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12/05/2022
Shared Differential Clustering across Single-cell RNA Sequencing Datasets with the Hierarchical Dirichlet Process
Single-cell RNA sequencing (scRNA-seq) is powerful technology that allow...
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09/30/2022
Leveraging variational autoencoders for multiple data imputation
Missing data persists as a major barrier to data analysis across numerou...
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08/26/2022
Mixtures of Gaussian Process Experts with SMC^2
Gaussian processes are a key component of many flexible statistical and ...
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06/22/2022
Bayesian nonparametric scalar-on-image regression via Potts-Gibbs random partition models
Scalar-on-image regression aims to investigate changes in a scalar respo...
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06/20/2021
Machine learning in the social and health sciences
The uptake of machine learning (ML) approaches in the social and health ...
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03/04/2021
On MCMC for variationally sparse Gaussian processes: A pseudo-marginal approach
Gaussian processes (GPs) are frequently used in machine learning and sta...
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05/30/2019
Enriched Mixtures of Gaussian Process Experts
Mixtures of experts probabilistically divide the input space into region...
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05/17/2019
Colombian Women's Life Patterns: A Multivariate Density Regression Approach
Women in Latin America and the Caribbean face difficulties related to th...
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04/04/2018
Posterior Inference for Sparse Hierarchical Non-stationary Models
Gaussian processes are valuable tools for non-parametric modelling, wher...
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03/28/2018
