Duffin, D., Cripps, E., Stemler, T., Girolami, M. (2021). Statistical finite elements for misspecified models. Proceedings of the National Academy of Sciences.
Wong, C.Y, Seshadri, P., Parks, G.T., Girolami, M. (2020). Embedded ridge approximations. Computer Methods in Applied Mechanics and Engineering.
Menberg, K., Bidarmaghz, A., Gregory, A., Choudhary, R., Girolami, M. (2020). Multi-fidelity approach to Bayesian parameter estimation in subsurface heat and fluid transport models. Science of the Total Environment.
Virtanen, S., Girolami, M. (2020). Spatio-Temporal Mixed Membership Models for Criminal Activity. Journal of the Royal Statistical Society Series A.
Povala, J., Virtanen, S., Girolami, M. (2020). Burglary in London: insights from statistical heterogeneous spatial point processes. Journal of the Royal Statistical Society Series C.
Virtanen, S., Girolami, M. (2020). Dynamic content based ranking. International Conference on Artificial Intelligence and Statistics.
Monterrubio-Gomez, K., Roininen, L., Wade, S., Damoulas, T., Girolami, M. (2020). Posterior inference for sparse hierarchical non-stationary models. Computational Statistics and Data Analysis.
Jans-Singh, M., Leeming, K., Choudhary, R., Girolami, M. (2020). Digital twin of an urban-integrated hydroponic farm. Data-Centric Engineering 1.
Dhada, M., Girolami, M., Parlikad, A.D. (2020). Anomaly detection in a fleet of industrial assests with hierarchical statistical modeling. Data-Centric Engineering 1.
Sacks, R., Brilakis, I., Pikas, E., Xie, H.S., Girolami, M. (2020). Construction with digital twin information systems. Data-Centric Engineering 1.
Girolami, M., Febrianto, E., Ying, G., Cirak, F. (2020). The statistical finite element method (statFEM) for coherent synthesis of observation data and model predictions. Computer Methods in Applied Mechanics and Engineering.
Cornish, R., Caterini, A., Deligiannidis, G., Doucet, A. (2020). Relaxing Bijectivity Constraints with Continuously Indexed Normalising Flows. International Conference on Machine Learning.
Finke, A., Doucet, A., Johansen, A.. (2020). Limit Theorems for Sequential Markov chain Monte Carlo Methods. Advances in Applied Probability.
Middleton, L., Deligiannidis, G., Doucet, A., Jacob, P.E. (2020). Unbiased MCMC for Intractable Target Distributions. Electronic Journal of Statistics.
Tadic, V.B, Doucet, A. (2020). Stability of Optimal Filter Higher-Order Derivatives. Stochastic Processes and Their Applications.
Heng, J., Bishop, A., Deligiannidis, G., Doucet, A. (2020). Controlled Sequential Monte Carlo. Annals of Statistics.
Gagnon, P.,Doucet, A. (2020). Non-reversible Jump Algorithms for Bayesian Nested Model Selection. Journal of Computational and Graphical Statistics.
Schmon, S., Deligiannidis, G.,Doucet, A., Pitt, M.K. (2020). Large Sample Asymptotics of the Pseudo-Marginal Method. Biometrika.
Heng, J.,Doucet, A., Pokern, Y. (2020). Gibbs Flow for Approximate Transport with Applications to Bayesian Computation. Journal of the Royal Statistical Society B.
Tadic, V.B,Doucet, A. (2020). Bias of Particle Approximations to Optimal Filter Derivative. SIAM Journal on Control and Optimization.
Maddison, C., Paulin, D., Teh, Y.W,Doucet, A. (2020). Dual Space Preconditioning for Gradient Descent. SIAM Journal on Optimization.
Tadic, V.,Doucet, A. (2020). Asymptotic Properties of Recursive Particle Maximum Likelihood Estimation. IEEE Transactions on Information Theory.
Deligiannidis, G., Paulin, D., Bouchard-Cote, A, Doucet, A. (2020). Randomized Hamiltonian Monte Carlo as Scaling Limit of the Bouncy Particle Sampler and Dimension-free Convergence Rates. Annals of Applied Probability.
Lee, A., Tiberi, S., Zanella, G. (2020). Unbiased approximations of products of expectations. Biometrika.
Lee, A., Singh, S., Vihola, M. (2020). Coupled conditional backward sampling particle filter. Annals of Statistics.
Andrieu, C., Yildirim, S., Doucet, A. (2020). Metropolis-Hastings with Averaged Acceptance Ratios. arxiv:2101.01253.
Andrieu, C., Lee, A., Livingstone, S. (2020). A general perspective on the Metropolis-Hastings kernel arxiv:2012.14881.
Andrieu, C., Dobson, P., Wang, A. (2020). Subgeometric hypocoercivity for piecewise-deterministic Markov process Monte Carlo methods. arxiv:2011.09341.
Crucinio, F., Doucet, A., Johansen, A. (2020). A particle method for solving Fredholm integral equations of the first kind. arxiv:2009.09974.
Brown, S., Jenkins, P.A., Johansen, A., Koskela, J. (2020). Simple conditions for convergence of sequential Monte Carlo genealogies with applications Electronic Journal of Probability.
Rendell, L.J, Johansen, A., Lee, A., Whiteley, N. (2020). Global consensus Monte Carlo Journal of Computational and Graphical Statistics.
Boustati, A., Akylidiz, D., Damoulas, T., Johansen, A. (2020). Generalized Bayesian filtering via sequential Monte Carlo. Advances in Neural Information Processing Systems.
Johansen, A. (2020). Sequential Monte Carlo: Particle filtering and beyond. Handbook of Computational Statistics and Data Science.
Nemeth, C., Fearnhead, P. (2020). Stochastic Gradient Markov Chain Monte Carlo. Journal of the American Statistical Association.
Chevallier, A., Fearnhead, P., Sutton, M. (2020). Reversible Jump PDMP Samplers for Variable Selection. arXiv:2010.11771.
Pollock, M., Fearnhead, P., Johansen, A., Roberts, G.O. (2020). The Scalable Langevin Exact Algorithm: Bayesian Inference for Big data. Journal of the Royal Statistical Society: Series B.
Andrieu, C., Durmus, A., Nüsken, N. and Roussel, J. (2019). Hypocoercivity of Piecewise Deterministic Markov Process-Monte Carlo. Annals of Applied Probability.
Andrieu, C. and Livingstone, S. (2019). Peskun-Tierney ordering for Markov chain and process Monte Carlo: beyond the reversible scenario. Annals of Statistics.
Angeli, L., Grosskinsky, S. and Johansen, A.M. (2019). Limit theorems for cloning algorithms. arXiv:1807.09288.
Bierkens, J., Fearnhead, P. and Roberts, G. (2019). The zig-zag process and super-efficient sampling for Bayesian analysis of big data. The Annals of Statistics, 47(3):1288-1320.
Bierkens, J., Roberts, G.O. and Zitt, P.-A. (2019). Ergodicity of the zigzag process. The Annals of Applied Probability, 29(4):2266-2301.
Dai, H., Pollock, M. and Roberts, G.O. (2019). Monte Carlo Fusion. Journal of Applied Probability, (To appear).
Ernst, P.A, Kendall, W.S., Roberts, G.O. and Rosenthal, J.S. (2019). MEXIT: Maximal un-coupling times for stochastic processes. Stochastic Processes and their Applications, 129(2):355-380.
Livingstone, S., Faulkner, M.F. and Roberts, G.O. (2019). Kinetic energy choice in Hamiltonian/hybrid Monte Carlo. Biometrika, 106(2):303-319.
Mider, M., Jenkins, P.A., Pollock, M. Roberts, G.O. and Sørensen, M. (2019). Simulating bridges using confluent diffusions. arXiv:1903.10184.
Pollock, M., Fearnhead, P., Johansen, A.M. and Roberts, G.O. (2020). The Scalable Langevin Exact Algorithm: Bayesian Inference for Big data (with discussion). Journal of the Royal Statistical Society: Series B, (To appear).
Rendell, L.J., Johansen, A.M., Lee, A. and Whiteley, N. (2019). Global consensus Monte Carlo. arXiv:1807.09288.
Tawn, N.G. and Roberts, G.O. (2019). Accelerating parallel tempering: Quantile tempering algorithm (QuanTA). Advances in Applied Probability, 51(3):802-834.
Wang, A.Q., Kolb, M., Roberts, G.O. and Steinsaltz, D. (2019). Theoretical properties of quasi-stationary Monte Carlo methods. The Annals of Applied Probability, 29(1):434-457.
Wang, A.Q., Pollock, M., Roberts, G.O. and Steinsaltz, D. (2019). Regeneration-enriched Markov processes with application to Monte Carlo. Annals of Applied Probability.
Wang, A.Q., Roberts, G.O. and Steinsaltz, D. (2019). An approximation scheme for quasi-stationary distributions of killed diffusions. Stochastic Processes and their Applications.
Yang, J., Roberts, G.O. and Rosenthal, J.S. (2019). Optimal Scaling of Metropolis Algorithms on General Target Distributions. arXiv:1904.12157.
Zanella, G. and Roberts, G.O. (2019). Scalable importance tempering and Bayesian variable selection. Scalable importance tempering and Bayesian variable selection.