果冻影院

* @ucl.ac.uk
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Profile

Ioanna Manolopoulou is a Professor of Statistical Science at the Department of Statistical Science, 果冻影院 as well as Associate Director of the HDRUK-Turing PhD programme. She is also affiliated to the recently-established 果冻影院 ELLIS unit.

Research Interests

Her main research interests lie in developing, extending or re-evaluating Bayesian models with a view to producing inferences which are useful and interpretable in practical applications. The focus is in flexible Bayesian modelling tools such as mixture models and tree models and applications of interest range from health data science to retail analytics.

Selected publications

• A. Caron, G. Baio and I. Manolopoulou (2022), 鈥淪parse Bayesian Causal Forests for Heterogeneous Treatment Effects Estimation鈥�, Journal of Computational and Graphical Statistics.
• A. Caron, G. Baio and I. Manolopoulou (2022), 鈥淓stimating Individual Treatment Effects using Non-Parametric Regression Models: a Review鈥�, Journal of the Royal Statistical Society, Series A
  Series A.
• M. Vega, J. O鈥橲ullivan, R. Prior, I. Manolopoulou and M. Musolesi (2022), 鈥淧osterior Summaries of Grocery Retail Topic Models: Evaluation, Interpretability and Credibility鈥�, Journal
  of the Royal Statistical Society, Series C.
• I. Manolopoulou,听A. Hille and B.C. Emerson (2019) 鈥淏PEC: An R package for Bayesian Phylogeographic and Ecological Clustering鈥�, accepted, Journal of Statistical Software.听
• J. Pitkin, G. Ross and I. Manolopoulou (2018) 鈥淒irichlet Process Mixture of Order Statistic Sequences with applications to Retail Analytics鈥�, Journal of Statistical Society, Series C.
• A. Heath, I. Manolopoulou and G. Baio (2017) 鈥淓fficient Monte Carlo Estimation of the Expected Value of Sample Information Using Moment Matching鈥�, Medical Decision Making.
• A. Heath, I. Manolopoulou and G. Baio (2016) 鈥淓fficient High-Dimensional Gaussian Process Regression to calculate the Expected Value of Partial Perfect Information in Health Economic Evaluations鈥�, Statistics in Medicine.
• P.R. Hahn, J. Murray and I. Manolopoulou (2016) 鈥淔lexible prior specification for partially iden- tified nonlinear regressions with binary responses鈥�, Journal of the American Statistical Association.
• I. Manolopoulou, M.P. Matheu, M.D. Cahalan, M. West and T.B. Kepler (2012). Bayesian Spatio-Dynamic Modelling in Cell Motility Studies: Learning Nonlinear Taxic Fields Guiding the Immune Response. Journal of the American Statistical Association, with discussion.