5 citations · 11 across the 8 of their papers we have counts for
8 papers
Variational Bayes latent class approach for EHR-based phenotyping with large real-world data
Brian Buckley, Adrian O'Hagan, Marie Galligan
Bayesian approaches to clinical analyses for the purposes of patient phenotyping have been limited by the computational challenges associated with applying the Markov-Chain Monte-C…
Joint modelling of the body and tail of bivariate data
Lídia M. André, Jennifer L. Wadsworth, Adrian O'Hagan
In situations where both extreme and non-extreme data are of interest, modelling the whole data set accurately is important. In a univariate framework, modelling the bulk and tail…
Copula Averaging for Tail Dependence in Insurance Claims Data
Sen Hu, Adrian O'Hagan
Analysing dependent risks is an important task for insurance companies. A dependency is reflected in the fact that information about one random variable provides information about…
AI-based Modeling and Data-driven Evaluation for Smart Manufacturing Processes
Mohammadhossein Ghahramani, Yan Qiao, MengChu Zhou +2
Smart Manufacturing refers to optimization techniques that are implemented in production operations by utilizing advanced analytics approaches. With the widespread increase in depl…
Bivariate Gamma Mixture of Experts Models for Joint Insurance Claims Modeling
Sen Hu, T Brendan Murphy, Adrian O'Hagan
In general insurance, risks from different categories are often modeled independently and their sum is regarded as the total risk the insurer takes on in exchange for a premium. Th…
Motor Insurance Accidental Damage Claims Modeling with Factor Collapsing and Bayesian Model Averaging
Sen Hu, Adrian O'Hagan, Thomas Brendan Murphy
Accidental damage is a typical component of motor insurance claim. Modeling of this nature generally involves analysis of past claim history and different characteristics of the in…
Investigation of Parameter Uncertainty in Clustering Using a Gaussian Mixture Model Via Jackknife, Bootstrap and Weighted Likelihood Bootstrap
Adrian O'Hagan, Thomas Brendan Murphy, Luca Scrucca +1
Mixture models are a popular tool in model-based clustering. Such a model is often fitted by a procedure that maximizes the likelihood, such as the EM algorithm. At convergence, th…
Improved model-based clustering performance using Bayesian initialization averaging
Adrian O'Hagan, Arthur White
The Expectation-Maximization (EM) algorithm is a commonly used method for finding the maximum likelihood estimates of the parameters in a mixture model via coordinate ascent. A ser…