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9 papers · 1 filter
Improving Software Engineering in Biostatistics: Challenges and Opportunities
Daniel Sabanés Bové, Heidi Seibold, Anne-Laure Boulesteix +9
Programming is ubiquitous in applied biostatistics; adopting software engineering skills will help biostatisticians do a better job. To explain this, we start by highlighting key c…
An approximate diffusion process for environmental stochasticity in infectious disease transmission modelling
Sanmitra Ghosh, Paul J. Birrell, Daniela De Angelis
Modelling the transmission dynamics of an infectious disease is a complex task. Not only it is difficult to accurately model the inherent non-stationarity and heterogeneity of tran…
Considering discrepancy when calibrating a mechanistic electrophysiology model
Chon Lok Lei, Sanmitra Ghosh, Dominic G. Whittaker +14
Uncertainty quantification (UQ) is a vital step in using mathematical models and simulations to take decisions. The field of cardiac simulation has begun to explore and adopt UQ me…
Distributed Computation for Marginal Likelihood based Model Choice
Alexander Buchholz, Daniel Ahfock, Sylvia Richardson
We propose a general method for distributed Bayesian model choice, using the marginal likelihood, where a data set is split in non-overlapping subsets. These subsets are only acces…
An n-dimensional Rosenbrock Distribution for MCMC Testing
Filippo Pagani, Martin Wiegand, Saralees Nadarajah
The Rosenbrock function is an ubiquitous benchmark problem for numerical optimisation, and variants have been proposed to test the performance of Markov Chain Monte Carlo algorithm…
MultiBUGS: A parallel implementation of the BUGS modelling framework for faster Bayesian inference
Robert J. B. Goudie, Rebecca M. Turner, Daniela De Angelis +1
MultiBUGS (https://www.multibugs.org) is a new version of the general-purpose Bayesian modelling software BUGS that implements a generic algorithm for parallelising Markov chain Mo…