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2023 T.Lowe, A. Golightly and C.Sherlock; Accelerating inference for stochastic-kinetic models; Computational Statistics and Data Analysis; arXiv | CSDA. Pub o/l Apr 2023.
2023 C.Sherlock, S.Urbas and M.Ludkin; The Apogee to Apogee Path Sampler; Journal of Computational and Graphical Statistics. code | arXiv | JCGS. Pub o/l Mar 2023.
2023 C.Sherlock and A.Golightly; Exact Bayesian inference for discretely observed Markov jump processes using finite rate matrices; Journal of Computational and Graphical Statistics 32 (1), 36-48. code | arXiv | JCGS. Pub o/l Aug 2022.
2022 C.Vyner, C.Nemeth and C.Sherlock; SwISS: a scalable Markov chain Monte Carlo divide-and-conquer strategy; Stat 12(1). arXiv | Stat. Pub o/l Nov 2022
2022 M.Ludkin and C.Sherlock; Hug and Hop: a discrete-time, non-reversible Markov chain Monte Carlo algorithm; Biometrika. code | arXiv | Biometrika. Pub o/l Jul 2022
2022 A.Golightly and C.Sherlock; Augmented pseudo-marginal Metropolis-Hastings for partially observed diffusion processes; Statistics and Computing, 32, Article number 21; arXiv | StCo. Pub o/l Feb 2022.
2022 (o/l 2021) C.Sherlock and A.Lee; Variance bounding of delayed-acceptance kernels; Methodology and Computing in Applied Probability, 24, 2237-2260. arXiv | code | MCAP. Pub o/l Nov 2021.
2022 (o/l 2021) C.Sherlock and A.Thiery; A discrete bouncy particle sampler; Biometrika, 109(2), 335-349. arXiv | Newton Institute talk | Biometrika. Pub o/l Feb 2021.
2022 (o/l 2021) R.Mountain and C.Sherlock; Recruitment prediction for multi-centre clinical trials based on a hierarchical Poisson-gamma model: asymptotic analysis and improved intervals; Biometrics, 78(2), 636-648. arXiv | Biometrics. Pub o/l Feb 2021.
2022 (o/l 2020) S.Urbas, C.Sherlock and P.Metcalfe; Interim recruitment prediction for multi-centre clinical trials; Biostatistics, 23(2), 485-506, kxaa036. arXiv | Biostatistcs. Pub o/l Sep 2020.
2021 C.Sherlock, A.Thiery and A.Golightly; Optimisation of delayed-acceptance pseudo-marginal random walk Metropolis algorithms; The Annals of Statistics 49(5) 2972-2990. arXiv | Ann. Stat. Pub o/l Oct 2021.
2021 C.Sherlock; Direct statistical inference for finite Markov jump processes via the matrix exponential; Computational Statistics 36, 2863-2887, 10.1007/s00180-021-01102-6. arXiv | code | Computational Statistics. Pub o/l Apr 2021.
2020 A.M.Barlow, C.Sherlock and J.Tawn; Inference for extreme values under threshold-based stopping rules; J. Roy. Stat. Soc. Series C, 69(4), 765-789. arXiv | JRSSC. Pub o/l Jun 2020.
2020, S.Taylor, C.Sherlock, G.Ridall and P.Fearnhead; Motor Unit Number Estimation via Sequential Monte Carlo ; Comp. Stats. and Data Analysis; 144, 106845, 16 pages. arXiv | CSDA. Pub o/l Oct 2019.
2019 R.Towe, J.Tawn, R.Lamb and C.Sherlock; Model-based inference of conditional extreme value distributions with hydrological applications; Environmentrics 30 (8) (e2575), 20 pages. arXiv | Env. Pub o/l Apr 2019.
2019 A.Golightly and C.Sherlock; Efficient sampling of conditioned Markov jump processes; Statistics and Computing 29(5), 1149-1163. arXiv | StCo. Pub o/l Feb 2019.
2018 C.Nemeth and C.Sherlock; Merging MCMC subposteriors through Gaussian-process approximations; Bayesian Analysis 13(2), 507-530. arXiv | BA. Pub o/l Aug 2017.
2017 C.Sherlock, A.Thiery and A.Lee; Pseudo-marginal Metropolis-Hastings using averages of unbiased estimators; Biometrika, 104(3), 727-734, asx031. doi: 10.1093/biomet/asx031. arXiv | Biometrika.
2017 G.A.Whitaker, A.Golightly, R.J.Boys and C.Sherlock; Improved bridge constructs for stochastic differential equations; Statistics and Computing 27(4) 885-900. doi:10.1007/s11222-016-9660-3. arXiv | StCo.
2017 G.A.Whitaker, A.Golightly, R.J.Boys and C.Sherlock; Bayesian inference for diffusion-driven mixed-effects models; Bayesian Analysis 12(2) 435-463. doi:10.1214/16-BA1009. arXiv | BA.
2017 C.Sherlock, A.Golightly and D.Henderson; Adaptive, delayed-acceptance MCMC for targets with expensive likelihoods; JCGS 26 434-444. doi:10.1080/10618600.2016.1231064. arXiv | JCGS.
2016 C.Nemeth, C.Sherlock and P.Fearnhead; Particle Metropolis adjusted Langevin algorithms; Biometrika, 103(3); 701-717. doi:10.1093/biomet/asw020. arXiv | Biometrika.
2016 C.Sherlock; Optimal scaling for the pseudo-marginal random walk Metropolis: insensitivity to the noise generating mechanism; MCAP 18(3), 869-884, 10.1007/s11009-015-9471-6. arXiv | MCAP.
2015 A.Golightly, D.Henderson, C.Sherlock; Delayed acceptance particle MCMC for exact inference in stochastic kinetic models; Statistics and Computing 25(5), 1039-1055. arXiv | StCo.
2015 C.Sherlock, A.Thiery, G.O.Roberts, J.S.Rosenthal; On the efficiency of pseudo-marginal random walk Metropolis algorithms; The Annals of Statistics, 43(1), 238-275. arXiv | Ann. Stat..
2014 C.Sherlock, A.Golightly, C.Gillespie; Bayesian inference for hybrid discrete-continuous stochastic kinetic models; Inverse Problems, 30 (2014) 114005, 22 pages. arXiv | IP.
2014 P.Fearnhead, V.Giagos, C.Sherlock; Inference for reaction networks using the Linear Noise Approximation; Biometrics, 70, 457-466. arXiv | Biometrics.
2014 T.Xifara, C.Sherlock, S.Livingstone, S.Byrne, M.Girolami; Langevin diffusions and the Metropolis-adjusted Langevin algorithm; Statistics and Probability Letters, 91, 14-19. arXiv | SPL.
2013 C.Sherlock, T.Xifara, S.Telfer, M.Begon; A hidden Markov model for disease interactions in a host; Journal of the Royal Statistical Society, Series C, 62(4), 609-627. arXiv | JRSSC.
2013 C. Sherlock; Optimal scaling of the random walk Metropolis: general criteria for the 0.234 acceptance rule; Journal of Applied Probability 50, 1-15. poster pdf | JAP.
2012 C. Sherlock and D. Elton; A class of spherical and elliptical distributions with Gaussian-like limit properties; Journal of Probability and Statistics 2012. JPS. Doi=10.1155/2012/467187.
2010 C. Sherlock, P. Fearnhead, G. Roberts; The random walk Metropolis: linking theory and practice through a case study; Statistical Science 25(2), 172-190. arXiv | Statistical Science.
2009 C. Sherlock, G. Roberts; Optimal scaling of the random walk Metropolis on elliptically symmetric unimodal targets; Bernoulli 15(3), 774-798. arXiv | Bernoulli.
2006 P. Fearnhead, C. Sherlock; An exact Gibbs sampler for the Markov modulated Poisson process. Journal of the Royal Statistical Society, Series B. 68(5), pp767-784. pdf | JRSSB.
2015 J. Lunn, C. Lewis, C.Sherlock; Impaired performance on advanced theory of mind tasks in children with epilepsy is related to poor communication and increased attention problems; Epilepsy and Behaviour 43 (2015) 109-116.
2014 D.C. Archer, C. Sherlock, D. Costain; An investigation into the effects of horse age, time and geographical variation on IFEE risk: a nested case-control study; PLOS ONE 10.1371/journal.pone.0112072 (19 pages) PLOS ONE.
2012 R. Cox, T. Su, H. Clough, M.J. Woodward and C. Sherlock; Spatial and temporal patterns in antimicrobial resistance of Salmonella Typhimurium amongst cattle in England and Wales; Epidemiology and Infection 140(11), 2062-2073.
2011 Williams, N.J., Sherlock C., Jones, T.R., Clough, H.E., Telfer, S.E., Begon, M., French, N.P., Bennett, M., and Hart, C.A.; The prevalence of antimicrobial resistant Escherichia coli in sympatric wild rodents varies by season and host; Journal of Applied Microbiology 110(4), 962-970. statistical appendix.
S.Malory and C.Sherlock; Residual-bridge constructs for conditioned diffusions; arXiv.
C. Sherlock; Methodology for inference on the Markov modulated Poisson process and theory for optimal scaling of the random walk Metropolis, PhD Thesis, Lancaster University. pdf.
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Comments, contributions and conferences
C. Sherlock (2020); Seconder contribution to the discussion of: Unbiased Markov chain Monte Carlo methods with couplings, JRSS Series B, 82 (3) JRSSB.
S. Sharples, D. Costain and C. Sherlock (2013); Predicting future offending in adolescents from a longitudinal survey with missing responses. In Proceedings of the 28th International Workshop on Statistical Modelling. (Eds: Muggeo, Capursi, Boscaino and Lovison).
S. Taylor, G. Ridall, C. Sherlock, P.Fearnhead (2013); Particle learning approach to Bayesian model selection: an application from neurology. In the Proceedings of the BAYSM conference in the series Springer Proceedings in Mathematics and Statistics. pdf.
P. Fearnhead, V. Giagos, C. Sherlock (2011); Comment on: Parameter inference for stochastic kinetic models of bacterial gene regulation: a Bayesian approach to systems biology., Bayesian Statistics 9, the Valencia 9 conference proceedings, Oxford University Press.
C. Sherlock, P. Fearnhead (2007); Comment on: Estimating the Integrated Likelihood via Posterior Simulation Using the Harmonic Mean Identity, Bayesian Statistics 8, the Valencia 8 conference proceedings, Oxford University Press.
C. Sherlock (2005); Contribution to discussion of Bayesian analysis of single-molecule experimental data, JRSS Series C 54, 500.