|Themes||Computational Statistics, Financial Risk, Econometrics and Stochastic Finance, General Theory and Methodology, Multivariate and High Dimensional Data, Stochastic Modelling and Time Series|
Before taking up his current post at UCL in October 2015, Petros Dellaportas held positions of professor, associate professor, senior lecturer and lecturer at the Athens University of Economics and Business, postdoctoral research assistant at the University of Nottingham and research assistant at the University of Plymouth. He has a Phd in Statistics from the University of Plymouth, an MSc in Statistics from the University of Sheffield and an undergraduate degree in Mathematics from the University of Athens.
Bayesian theory and applications, financial modelling, machine learning.
- Dellaportas P. and Kontoyiannis I. (2012). Control variates for reversible MCMC Samplers. Journal of the Royal Statistical Society, Series B. 74, 1, 133-161.
- Dellaportas P., Forster J.J. and Ntzoufras I. (2012). Specification of prior distributions under model uncertainty. Statistical Science, 27,2,232-246
- Papathomas M., Dellaportas P. And Vasdekis V.G.S. (2011). A novel reversible jump algorithm for generalized linear models. Biometrika, 98, 231-236.
- Kalogeropoulos K., Roberts G.O. and Dellaportas P. (2010). Inference for stochastic volatility models using time change transformations Annals of Statistics, 38, 2, 784-807.
- Dellaportas P., Friel N. and Roberts G.O. (2006). Bayesian model selection for partially observed diffusion models. Biometrika.93,4,809-825.
- Roberts G.O., Papaspiliopoulos O. and Dellaportas P (2004). Bayesian inference for Non-Gaussian Ornstein-Uhlenbeck Stochastic Volatility processes. Journal of the Royal Statistical Society, Series B, 66, 369-393.
- Dellaportas P. and Tarantola C. (2005). Categorical data squashing by combining factor levels. Journal of the Royal Statistical Society, Series B, 67,269-283.
- Dellaportas P and Forster J J (1999) Markov chain Monte Carlo Model Determination for Hierarchical and Graphical Log-linear models. Biometrika, 86, 615-633.