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May 2017
  • 11 May 17
    Speaker: Arnaud Doucet, Department of Statistics, University of Oxford Title: What’s new in Monte Carlo
  • 08 May 17
    Speaker: Jeffrey Rosenthal, University of Toronto Title: Conditions for Convergence of Adaptive MCMC Algorithms Abstract: Markov chain Monte Carlo (MCMC) algorithms, such as the Metropolis Algorithm and the Gibbs Sampler, are an extremely popular method of approximately sampling from complicated probability distributions. Adaptive MCMC attempts to automatically modify the algorithm while it runs, to improve its performance on t...
  • 04 May 17
    Speaker: Patrick Rubin-Delanchy, Department of Statistics, University of Oxford Title: Big Network Modeling and Anomaly Detection for Cyber-Security Applications Abstract: Data arising in cyber-security applications often have a network, or `graph-like’, structure, and accurate statistical modelling of connectivity behaviour has important implications, for instance, for network intrusion detection. We present a linear a...
April 2017
  • 27 Apr 17
    Speaker: Cecilia Lingren, Nuffield Department of Medicine, University of Oxford Title: Genetics of obesity and human fat distribution Abstract: Prof. Cecilia Lindgren is a Senior Group Leader at the Big Data Institute, University of Oxford. Her research focuses on applying genomics to dissect the etiology of obesity related traits and their relationship with reproductive health. In this seminar, Prof Lindgren will:
  • 24 Apr 17
    Speaker: Amaury Lambert, Laboratoire de Probabilités & Modèles Aléatoires, UPMC Univ Paris 06 Title: Random ultrametric trees and applications Abstract: Ultrametric trees are trees whose leaves lie at the same distance from the root. They are used to model the genealogy of a population of particles co-existing at the same point in time. We show how the boundary of an ultrametric tree, like any compact ultrametric space, ...
March 2017
  • 10 Mar 17
    Speaker: Professor Martin Hairer, University of Warwick Title: A BPHZ theorem for stochastic PDEs Abstract: A classical result obtained in the 50’s and 60’s by Bogoliubov, Parasiuk, Hepp and Zimmerman provides a prescription on how to renormalise amplitudes of Feynman diagrams arising in perturbative quantum field theory in a consistent way. We will discuss an analogue of this theorem which has both an analytic an...
  • 02 Mar 17
    Speaker: Alex Bouchard-Cote, Department of Statistics, The University of British Columbia Title: Statistical/computational phylogenetics Abstract: I will start with a basic introduction to phylogenetics from a statistical and computational point of view. I will then describe some non-standard motivating applications in historical linguistics and cancer genetics. Finally, I will cover a methodology motivated by challenges enco...
February 2017
  • 23 Feb 17
    Speaker: Jouni Helske, University of Jyvaskyla Title: Computationally efficient state space modelling Abstract: State space modelling (SSMs) offers an unified framework for statistical inference of a broad class of time series and other data. For example, traditional ARIMA models, structural time series models, and generalized linear mixed models can all be represented in a state space form. In this talk I will first in...
  • 16 Feb 17
    Speaker: Christina Goldschmidt, Department of Statistics, University of Oxford Title: Stable Lévy processes and forests
January 2017
  • 27 Jan 17
    Speaker: Professor Jean-Philippe Vert, Mines ParisTech, France Title: Machine learning for patient stratification from genomic data Abstract: As the cost and throughput of genomic technologies reach a point where DNA sequencing is close to becoming a routine exam at the clinics, there is a lot of hope that treatments of diseases like cancer can dramatically improve by a digital revolution in medicine, where smart algorithms a...