107 citations · 162 across the 14 of their papers we have counts for
6 papers · 1 filter
Likelihood free inference for Markov processes: a comparison
Jamie Owen, Darren J. Wilkinson, Colin S. Gillespie
Approaches to Bayesian inference for problems with intractable likelihoods have become increasingly important in recent years. Approximate Bayesian computation (ABC) and "likelihoo…
Diagnostics for assessing the linear noise and moment closure approximations
Colin S. Gillespie, Andrew Golightly
Solving the chemical master equation exactly is typically not possible, so instead we must rely on simulation based methods. Unfortunately, drawing exact realisations, results in s…
A complete data frame work for fitting power law distributions
Colin S. Gillespie
Over the last few decades power law distributions have been suggested as forming generative mechanisms in a variety of disparate fields, such as, astrophysics, criminology and data…
Fitting heavy tailed distributions: the poweRlaw package
Colin S Gillespie
Over the last few years, the power law distribution has been used as the data generating mechanism in many disparate fields. However, at times the techniques used to fit the power…
Scalable Inference for Markov Processes with Intractable Likelihoods
Jamie Owen, Darren J. Wilkinson, Colin S. Gillespie
Bayesian inference for Markov processes has become increasingly relevant in recent years. Problems of this type often have intractable likelihoods and prior knowledge about model r…
Bayesian Inference for Hybrid Discrete-Continuous Stochastic Kinetic Models
Chris Sherlock, Andrew Golightly, Colin Gillespie
We consider the problem of efficiently performing simulation and inference for stochastic kinetic models. Whilst it is possible to work directly with the resulting Markov jump proc…