activity
20172020
collaborators

5 papers

stat.AP2020

Parameter inference for a stochastic kinetic model of expanded polyglutamine proteins

Holly F. Fisher, Richard J. Boys, Colin S. Gillespie +2

The presence of protein aggregates in cells is a known feature of many human age-related diseases, such as Huntington's disease. Simulations using fixed parameter values in a model…

stat.CO2018

Bayesian inference for a partially observed birth-death process using data on proportions

Richard J. Boys, Holly F. Ainsworth, Colin S. Gillespie

Stochastic kinetic models are often used to describe complex biological processes. Typically these models are analytically intractable and have unknown parameters which need to be…

stat.CO2018

Efficient construction of Bayes optimal designs for stochastic process models

Colin S. Gillespie, Richard J. Boys

Stochastic process models are now commonly used to analyse complex biological, ecological and industrial systems. Increasingly there is a need to deliver accurate estimates of mode…

stat.CO2018

Correlated pseudo-marginal schemes for time-discretised stochastic kinetic models

Andrew Golightly, Emma Bradley, Tom Lowe +1

The challenging problem of conducting fully Bayesian inference for the reaction rate constants governing stochastic kinetic models (SKMs) is considered. Given the challenges underl…

stat.AP2017

Estimating the number of casualties in the American Indian war: a Bayesian analysis using the power law distribution

Colin S Gillespie

The American Indian war lasted over one hundred years, and is a major event in the history of North America. As expected, since the war commenced in late eighteenth century, casual…