The research carried out under this theme covers foundational and theoretical aspects of probability and inferential statistics, and generic statistical methodology. Current research interests include:
- Philosophical foundations of probability and statistics
- Theory of inference, including Bayesian theory, predictive inference and asymptotic theory
- Core Bayesian methodology
- Statistical methodology for causal inference
- Inference for stochastic models, nonparametric and semiparametric inference
- Methodology for multivariate data, including cluster analysis, longitudinal data analysis, multivariate calibration and classification
- Machine learning, classification, pattern recognition
- Decision analysis via operational research and financial methods
|Ardo van den Hout (Theme Lead)||Change-point models; joint models; longitudinal data analysis; multi-state models; survival analysis|
|Gianluca Baio||Bayesian analysis; causal inference from observational data; evaluation of interventions|
Sequential Monte-Carlo; Markov Chain Monte-Carlo; Bayesian Statistics; Computational Statistics; Monte-Carlo algorithms in High Dimensions; Inverse Problems; Data Assimilation; Inference, Applications and Simulation for SDEs; Fractional and White Noise in Econometrics; Copulas; Hidden Markov Models; Biostatistics; Stochastic Volatility Models; Networks; Gaussian Graphical Models
|Richard Chandler||Climatology; environmental sciences; estimating functions; hydrology; inference for dependent data|
|Codina Cotar||Optimal transport; random graphs; reinforced random walks; statistical mechanics|
|Petros Dellaportas||Bayesian model choice; graphical models; theory of MCMC; variational inference|
|Tom Fearn||Bayesian methods; calibration; classification; multivariate analysis; near infrared spectroscopy|
|Jim Griffin||Bayesian methods; high-dimensional data; variable selection; Bayesian nonparametrics|
|Simon Harden||Foundations, inference|
|Elinor Jones||Causal inference from observational data; privacy preserving analysis of federated data|
|Samuel Livingstone||MCMC; Markov chains; Bayesian computation; theory of inference; probabilistic modelling of complex datasets|
Bayesian methods, mixture models, sampling bias, Bayesian nonparametrics, diffusion modelling
|Giampiero Marra||Penalised likelihood; microeconomics; splines; simultaneous equation systems; social sciences; unobserved confouding|
|Yvo Pokern||High-dimensional Gaussian Markov random fields; inference for partially observed and hypoelliptic diffusion processes; nonparametric inference for stochastic differential equations; sequential Monte Carlo methods|
|Matina Rassias||Stochastic analysis|
|Graphical models; causal inference; random networks; algebraic statistics|
|Afzal Siddiqui||Energy economics; game theory; optimisation; real options|
|Tengyao Wang||High-dimensional inference; change-point analysis; shape-contrained estimation|
|Francois-Xavier Briol||Bayesian methods; computational statistics; intractable likelihoods; kernel methods|
|Jinghao Xue||Hybrid discriminative-generative classification; imbalanced learning; statistical pattern recognition|
Current and Recent Externally Funded Projects
- DAMS 2.0: Design and assessment of resilient and sustainable interventions in water-energy-food-environment Mega-Systems, £8162k, ESRC ES/P011373/1, Oct 2017-Dec 2021, CI: Siddiqui.
- Sequential Monte Carlo Methods for Applications in High Dimensions, £98,868, EPSRC First Grant EP/J01365X/1, Jul 2012 - Jun 2013, PI: A. Beskos.
- STRIDES: Strategic transmission and renewable investment in a decentralized electricity sector, C$123.5k, Social Sciences and Humanities Research Council of Canada 435-2017-0068, April 2017-Mar 2020, collaborator: Siddiqui
- Detecting anomalies in the VAT network, funded by the Turing-HSBC-ONS Economic Data Science Awards 2018, started Feb 2019, PI: Dellaportas.
- Forecasting with large macroeconomic and financial datasets, funded by the Turing-HSBC-ONS Economic Data Science Awards 2018, started Feb 2019, CI: Dellaportas.
- PlanFES: Advanced analytics for planning future energy systems, €150k, Aalto Science Institute, Jan 2019-Dec 2021, CI: Siddiqui
Leverhulme Trust Prize in Mathematics & Statistics 2015, £100,000, Feb 2015 - Aug 2019, PI: Beskos.
Health economic evaluation of Pembrolizumab in Melanoma. Merck, Sharpe & Dohme Research Grant, £190,000, 2017 - 2018, PI: Baio.
Advanced Stochastic Computation for Inference from Tree, Graph and Network Models, £408,546, EPSRC EP/K01501X/1, Oct 2013 - Feb 2018, Co-I: Beskos.
Statistical modelling and estimation for spatiotemporal data with oceanographic applications, €294k, EU FP7 626775, Apr 2014 - Mar 2017, PI: Olhede.
- Characterizing Interactions Across Large-Scale Point Process Populations, £152k, EPSRC EP/L001519/1, Jul 2013 - Jun 2015, PI: Olhede.
- A multicriterion approach for cluster validation, £98k, EPSRC EP/K033972/1, Jun 2013 - May 2017, PI: Hennig.
- Information geometry for Bayesian hierarchical models, £238k, EPSRC EP/K005723/1, Mar 2013 - Mar 2016, PI: Byrne.
- Probability, Uncertainty and Risk in the Natural Environment, £683k, NERC NE/J017434/1, Aug 2012 - Aug 2016, PI: Chandler.
- Semiparametric Sample Selection Modelswith Applications in Biostatistics, Economics and Environmetrics, £100k, EPSRC EP/J006742/1, May 2012 - Jun 2013, PI: Marra.
- High Dimensional Models for Multivariate Time Series Analysis, £990k, EPSRC EP/I005250/1, Oct 2010 - Sep 2015, PI: Olhede.
EnRiMa - Energy and Risk Management in Public Buildings, €3500k, EC FP7 260041, Oct 2010 - Mar 2014, CI: Siddiqui.
- Sequential Monte Carlo Methods for Applications in High Dimensions, £98,868, EPSRC EP/J01365X/1, Jul 2012 - Jun 2013, PI: Beskos