9 citations · 18 across the 9 of their papers we have counts for
9 papers
Exploring complex dependence structures using Bayesian bi-clustering and log-linear graphical modelling
Michail Papathomas
Bayesian partitioning is utilised simultaneously on subjects and categorical variables to reveal complex dependence structures. Clusters of variables are referred to as views. Vari…
Variance matrix priors for Dirichlet process mixture models with Gaussian kernels
Wei Jing, Michail Papathomas, Silvia Liverani
The Dirichlet Process Mixture Model (DPMM) is a Bayesian non-parametric approach widely used for density estimation and clustering. In this manuscript, we study the choice of prior…
Introducing a Real-time Interactive GUI Tool for Visualization of Galaxy Spectra
Ho-Hin Leung, Vivienne Wild, Adam Carnall +1
To aid the understanding of the non-linear relationship between galaxy properties and predicted spectral energy distributions (SED), we present a new interactive graphical user int…
Parameter Redundancy and the Existence of Maximum Likelihood Estimates in Log-linear Models
Serveh Sharifi Far, Michail Papathomas, Ruth King
Log-linear models are typically fitted to contingency table data to describe and identify the relationship between different categorical variables. However, the data may include ob…
On the correspondence of deviances and maximum likelihood and interval estimates from log-linear to logistic regression modelling
Wei Jing, Michail Papathomas
Consider a set of categorical variables where at least one, denoted by , is binary. The log-linear model that describes the counts in the resulting contingency tab…
On synthetic interval data with predetermined subject partitioning, and partial control of the variables' marginal correlation structure
Michail Papathomas
A standard approach for assessing the performance of partition models is to create synthetic data sets with a prespecified clustering structure, and assess how well the model revea…
On the correspondence from Bayesian log-linear modelling to logistic regression modelling with -priors
Michail Papathomas
Consider a set of categorical variables where at least one of them is binary. The log-linear model that describes the counts in the resulting contingency table implies a specific l…
Exploring dependence between categorical variables: benefits and limitations of using variable selection within Bayesian clustering in relation to log-linear modelling with interaction terms
Michail Papathomas, Sylvia Richardson
This manuscript is concerned with relating two approaches that can be used to explore complex dependence structures between categorical variables, namely Bayesian partitioning of t…
PReMiuM: An R Package for Profile Regression Mixture Models using Dirichlet Processes
Silvia Liverani, David I. Hastie, Lamiae Azizi +2
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 lin…