"All of Statistics", unashamedly stealing the famous book-title of Larry Wasserman to describe this seminar series as we intend to bring together everyone working in statistics with different hats on your head (statistician, mathematician, probabilist, machine learner etc.). -- Organisers.
Time: Runs on every other Monday 13:00-14:00 during term time. (In between weeks we will have Lunch time seminars during lunch with informal talks)
Lunch: Free lunch every Monday (12:00-13:00) in Statistics common room on the 1st floor of Mathematical Sciences Building.
Venue: Statistics common room at Mathematical Sciences Building (in-person only)
Inaugural talk on 23rd October would be given by our very own Professor Gareth Roberts.
Title: Bayesian Fusion
Abstract: Suppose we can readily access samples from but we wish to obtain samples from . The so-called Bayesian Fusion problem comes up within various areas of modern Bayesian Machine Learning, for example in the context of big data or privacy constraints, as well as more traditional statistical areas such as meta-analysis. Many approximate solutions to this problem have been proposed. However this talk will present an exact solution based on rejection sampling in an extended state space, where the accept/reject decision is carried out by simulating the skeleton of a suitably constructed auxiliary collection of Brownian bridges.
On 6th November we would have Professor Johannes Schmidt-Hieber visiting us from
But how could we extend this work so that it might apply to produce predictive models of what might happen when the decision maker believes that his controls might be resisted? In this talk I will argue that standard causal models then need to be generalised to embed a decision maker's beliefs of the intent capability and the information a resistant adversary might have about the intervention after it has been made. After reviewing recent advances in general forms of Bayesian dynamic causal models I will describe how - using a special form of Adversarial Risk Analysis - we are developing new intelligent algorithms to produce such predictions. The talk will be illustrated throughout by examples of various adversarial threats currently being analysed within the UK.
(This is joint work with Kate Lee, Jessie Jiang, Nicholas Karn, David Johnson, Alexis Muir-Watt and Rukuang Huang.)
On 8th January we would have Professor Fabrizio Leisen visiting us from the University of Nottingham.
On 22nd January the talk would be given by our very own Professor Anastasia Papavasiliou.
On 19th February we would have Professor Terry Lyons visiting us from the University of Oxford.
On 4th March we would have Dr. Fanghui Liu from the Computer Science department of University of Warwick.