10 citations · 10 across the 4 of their papers we have counts for
7 papers
Tail-Greedy Unbalanced Haar Wavelet Segmentation for Copy Number Alteration Data
Maharani Ahsani Ummi, Stuart Barber, Henry M. Wood +1
Detecting copy number alterations (CNAs) from next-generation sequencing data remains challenging, particularly for short segments under noisy conditions. Existing segmentation met…
Defining Utility as a Measure of Preference Under Uncertainty in Phase I-II Oncology Dose Finding Trials
Andrew Hall, Duncan Wilson, Stuart Barber +1
The main objective of dose finding trials is to find an optimal dose amongst a candidate set for further research. The trial design in oncology proceeds in stages with a decision a…
Mixture models for spherical data with applications to protein bioinformatics
Kanti V. Mardia, Stuart Barber, Philippa M. Burdett +2
Finite mixture models are fitted to spherical data. Kent distributions are used for the components of the mixture because they allow considerable flexibility. Previous work on such…
Gaussian process modeling of heterogeneity and discontinuities using Voronoi tessellations
Christopher A. Pope, John Paul Gosling, Stuart Barber +4
Many methods for modelling spatial processes assume global smoothness properties; such assumptions are often violated in practice. We introduce a method for modelling spatial proce…
Bayesian Protein Sequence and Structure Alignment
Christopher Fallaize, Peter Green, Kanti Mardia +1
The structure of a protein is crucial in determining its functionality, and is much more conserved than sequence during evolution. A key task in structural biology is to compare pr…
Bayesian alignment of similarity shapes
Kanti V. Mardia, Christopher J. Fallaize, Stuart Barber +2
We develop a Bayesian model for the alignment of two point configurations under the full similarity transformations of rotation, translation and scaling. Other work in this area ha…
The Rate of Convergence for Approximate Bayesian Computation
Stuart Barber, Jochen Voss, Mark Webster
Approximate Bayesian Computation (ABC) is a popular computational method for likelihood-free Bayesian inference. The term "likelihood-free" refers to problems where the likelihood…