In this project we consider the challenging task of developing fully Bayesian sparse analyses for the situations when the numbers of predictors is larger than observations for complex responses and covariates grouped by blocks with the sparsity for blocks and cases.
Invited talks, refereed proceedings and other conference outputs
Lee, X. J., Fulford G. R., Pettitt A.N., & Ruggeri F.
(2017). A stochastic model for MRSA transmission within a hospital ward incorporating environmental contamination. Epidemiology and Infection. 145(4), 825-838. doi: 10.1017/S0950268816002880
Ryan, C. M., Drovandi C. C., & Pettitt A.N.
(2016). Optimal Bayesian experimental design for models with intractable likelihoods using indirect inference applied to biological process models. Bayesian Analysis. 11(3), 857-883. doi: 10.1214/15-BA977
Drovandi, C. C., Pettitt A.N., & McCutchan R. A.
(2016). Exact and approximate Bayesian inference for low integer-valued time series models with intractable likelihoods. Bayesian Analysis. 11(2), 325-352. doi: 10.1214/15-BA950
McGrory, C.A., Pettitt A.N., Titterington D.M., Alston C.L., & Kelly M.
(2016). Transdimensional sequential Monte Carlo using variational Bayes — SMCVB. Computational Statistics & Data Analysis. 93, 246-254. doi: 10.1016/j.csda.2015.03.006
Falk, M. G., Alston C. L., McGrory C. A., Clifford S., Heron E. A., Leonte D., et al.
(2015). Recent Bayesian approaches for spatial analysis of 2-D images with application to environmental modelling. Environmental and Ecological Statistics. 22(3), 571-600. doi: 10.1007/s10651-015-0311-1