Statistical Science


General Theory and Methodology

Theme Overview

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

Theme members

Ardo van den Hout (Theme Lead)Change-point models; joint models; longitudinal data analysis; multi-state models; survival analysis
Gianluca BaioBayesian analysis; causal inference from observational data; evaluation of interventions
Alex Beskos

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 ChandlerClimatology; environmental sciences; estimating functions; hydrology; inference for dependent data
Codina CotarOptimal transport; random graphs; reinforced random walks; statistical mechanics
Petros DellaportasBayesian model choice; graphical models; theory of MCMC; variational inference
Tom FearnBayesian methods; calibration; classification; multivariate analysis; near infrared spectroscopy
Jim GriffinBayesian methods; high-dimensional data; variable selection; Bayesian nonparametrics
Simon HardenFoundations, inference
Elinor JonesCausal inference from observational data; privacy preserving analysis of federated data
Samuel LivingstoneMCMC; Markov chains; Bayesian computation; theory of inference; probabilistic modelling of complex datasets
Ioanna Manolopoulou

Bayesian methods, mixture models, sampling bias, Bayesian nonparametrics, diffusion modelling

Giampiero MarraPenalised likelihood; microeconomics; splines; simultaneous equation systems; social sciences; unobserved confouding
Yvo PokernHigh-dimensional Gaussian Markov random fields; inference for partially observed and hypoelliptic diffusion processes; nonparametric inference for stochastic differential equations; sequential Monte Carlo methods
Matina RassiasStochastic analysis
F. Javier RubioBayesian theory;  Survival analysis; Longitudinal data analysis; Model and variable selection; Biostatistics

Kayvan Sadeghi

Graphical models; causal inference; random networks; algebraic statistics
Afzal SiddiquiEnergy economics; game theory; optimisation; real options
Tengyao WangHigh-dimensional inference; change-point analysis; shape-contrained estimation 
Francois-Xavier BriolBayesian methods; computational statistics; intractable likelihoods; kernel methods
Jinghao XueHybrid 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