Petros Dellaportas



Petros Dellaportas has a joint appointment as a professor in Statistical Science in the department of Statistical Science, University College London and as a professor of Statistics in the department of Statistics, Athens University of Economics and Business. He is also a Turing fellow of the Alan Turing Institute.

Research interests : Bayesian theory and applications, financial modelling, machine learning.

Professor Dellaportas' Google Scholar profile is here.


Recent articless:

Hirt, M. and Dellaportas P. (2018) Scalable Bayesian Learning for State Space Models using Variational Inference with SMC Samplers.

Alexopoulos A., Dellaportas P. and Forster J.J. (2018) Bayesian forecasting of mortality rates using latent Gaussian models.

Dellaportas P., Plataniotis A. and Titsias M. Scalable inference for a full multivariate stochastic volatility model.

Dellaportas, P. and Tsionas MG (2017). Importance sampling from posterior distributions using copula-like approximations. To appear in the Journal of Econometrics.

Finke, Axel, Ruth King, Alexandros Beskos, and Petros Dellaportas. "Efficient sequential Monte Carlo algorithms for integrated population models." arXiv preprint arXiv:1708.04221 (2017).

Owen JL Rackham, Sarah R Langley, Thomas Oates, Eleni Vradi, Nathan Harmston, Prashant K Srivastava, Jacques Behmoaras, Petros Dellaportas, Leonardo Bottolo, and Enrico Petretto. A Bayesian Approach for Analysis of Whole-Genome Bisulfite Sequencing Data Identifies DiseaseAssociated Changes in DNA Methylation (2017). Genetics 205.4, 1443-1458.


Last Greek Stochastics conference:

Greek Stochastics ι : Model determination, Milos, Greece, 14-17 July 2017. Lectures by David Rossell, Judith Rousseau and Dimitris Politis














Address for mail:
Department of Statistical Science,
University College ,
Gower Street ,
London, WC1E 6BT



Address for visiting:
Department of Statistical Science,
University College ,
1-19 Torrington Place,
London WC1E 7HB



Email:
p dot surname at ucl.ac.uk

Tel:
+44(0)2031083244