Overview
Background
Matt Sutton is a statistician and Bayesian computation researcher specialising in advanced Monte Carlo methods for complex and high-dimensional models. Currently a lecturer in mathematics and statistics at the University of Queensland, he develops new approaches to scalable Bayesian inference through piecewise deterministic Markov processes (PDMPs) and related non-reversible algorithms.
He holds an ARC DECRA fellowship on Scalable Bayesian inference for secure and reliable decision making and is a Chief Investigator on the ARC Discovery Project Fixing the holes in Bayesian model comparison. His research focuses on enhancing the efficiency and reliability of simulation-based inference by leveraging continuous-time dynamics, gradient-driven sampling, and robust model comparison methods for modern Bayesian computation.
Availability
- Dr Matthew Sutton is:
- Available for supervision
Qualifications
- Doctor of Philosophy of Mathematics Statistics, Queensland University of Technology
Works
Search Professor Matthew Sutton’s works on UQ eSpace
Featured
2023
Journal Article
Concave-convex PDMP-based sampling
Sutton, Matthew and Fearnhead, Paul (2023). Concave-convex PDMP-based sampling. Journal of Computational and Graphical Statistics, 32 (4), 1425-1435. doi: 10.1080/10618600.2023.2203735
Featured
2023
Conference Publication
Transport Reversible Jump Proposals
Davies, Laurence, Salomone, Robert, Sutton, Matthew and Drovandi, Christopher (2023). Transport Reversible Jump Proposals. 26th International Conference on Artificial Intelligence and Statistics, Valencia, Spain, 25 - 27 April 2023. United States: ML Research Press.
Featured
2022
Journal Article
Reversible jump PDMP samplers for variable selection
Chevallier, Augustin, Fearnhead, Paul and Sutton, Matthew (2022). Reversible jump PDMP samplers for variable selection. Journal of the American Statistical Association, 118 (544), 2915-2927. doi: 10.1080/01621459.2022.2099402
Featured
2022
Journal Article
Leveraging pleiotropic association using sparse group variable selection in genomics data
Sutton, Matthew, Sugier, Pierre-Emmanuel, Truong, Therese and Liquet, Benoit (2022). Leveraging pleiotropic association using sparse group variable selection in genomics data. BMC Medical Research Methodology, 22 (1) 9. doi: 10.1186/s12874-021-01491-8
Featured
2022
Conference Publication
Continuously-Tempered PDMP samplers
Sutton, Matthew, Salomone, Robert, Chevallier, Augustin and Fearnhead, Paul (2022). Continuously-Tempered PDMP samplers. 36th Conference on Neural Information Processing Systems (NeurIPS 2022), New Orleans, LA United States, 28 November - 9 December 2022.
Featured
2017
Journal Article
Bayesian variable selection regression of multivariate responses for group data
Liquet, Benoit, Mengersen, Kerrie L., Pettitt, Anthony N. and Sutton, Matthew (2017). Bayesian variable selection regression of multivariate responses for group data. Bayesian Analysis, 12 (4), 1039-1067. doi: 10.1214/17-ba1081
2025
Journal Article
Debiasing piecewise deterministic Markov process samplers using couplings
Corenflos, Adrien, Sutton, Matthew and Chopin, Nicolas (2025). Debiasing piecewise deterministic Markov process samplers using couplings. Scandinavian Journal of Statistics, 52 (4), 1932-1974. doi: 10.1111/sjos.70015
2025
Conference Publication
The polynomial Stein discrepancy for assessing moment convergence
Srinivasan, Narayan, Sutton, Matthew, Drovandi, Christopher and South, Leah F. (2025). The polynomial Stein discrepancy for assessing moment convergence. International Conference on Machine Learning (ICML 2025), Vancouver, Canada, 13–19 July 2025. San Diego, CA, United States: ICML.
2023
Journal Article
Bayesian detectability of induced polarization in airborne electromagnetic data
Davies, L., Ley-Cooper, A. Y., Sutton, M. and Drovandi, C. (2023). Bayesian detectability of induced polarization in airborne electromagnetic data. Geophysical Journal International, 235 (3), 2499-2523. doi: 10.1093/gji/ggad073
2020
Book Chapter
Bayesian variable selection
Sutton, Matthew (2020). Bayesian variable selection. Case studies in applied Bayesian data science: CIRM Jean-Morlet Chair, Fall 2018. (pp. 121-135) edited by Kerrie L. Mengersen, Pierre Pudlo and Christian P. Robert. Cham, Switzerland: Springer. doi: 10.1007/978-3-030-42553-1_5
2019
Journal Article
PLS for Big Data: A unified parallel algorithm for regularised group PLS
Lafaye de Micheaux, Pierre, Liquet, Benoît and Sutton, Matthew (2019). PLS for Big Data: A unified parallel algorithm for regularised group PLS. Statistics Surveys, 13, 119-149. doi: 10.1214/19-ss125
2018
Journal Article
[HDDA] sparse subspace constrained partial least squares
Sutton, Matthew, Mengersen, Kerrie and Liquet, Benoit (2018). [HDDA] sparse subspace constrained partial least squares. Journal of Statistical Computation and Simulation, 89 (6), 1005-1019. doi: 10.1080/00949655.2018.1555830
2018
Journal Article
Sparse partial least squares with group and subgroup structure
Sutton, Matthew, Thiébaut, Rodolphe and Liquet, Benoît (2018). Sparse partial least squares with group and subgroup structure. Statistics in Medicine, 37 (23), 3338-3356. doi: 10.1002/sim.7821
2014
Journal Article
Lambda-fold theta graphs: Metamorphosis into 6-cycles
Billington, Elizabeth J., Khodkar, Abdollah, Petrusma, Dylan and Sutton, Matthew (2014). Lambda-fold theta graphs: Metamorphosis into 6-cycles. AKCE International Journal of Graphs and Combinatorics, 11 (1), 81-94.
2014
Journal Article
On the metamorphosis of a G-design into a (G - e) -design
Sutton, Matthew William (2014). On the metamorphosis of a G-design into a (G - e) -design. Discrete Mathematics, 318 (1), 71-77. doi: 10.1016/j.disc.2013.11.014
Funding
Current funding
Supervision
Availability
- Dr Matthew Sutton is:
- Available for supervision
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Media
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