Terms 1-3, Location MB0.08, Fridays 12:15-14:00 (12:15-12:45 is an informal sandwich lunch).
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2019/20 Term 1:
- Week 1 - 4th October - Felipe Medina Aguayo (Reading) - "Revisiting the balance heuristic for estimating normalising constants"
- Abstract: Multiple importance sampling estimators are widely used for computing intractable constants due to its reliability and robustness. The celebrated balance heuristic estimator belongs to this class of methods and has proved very successful in computer graphics. The basic ingredients for computing the estimator are: a set of proposal distributions, indexed by some discrete label, and a predetermined number of draws from each of these proposals. However, if the number of available proposals is much larger than the number of permitted importance points, one needs to select, possibly at random, which of these distributions will be used. We explore some improvements and variations of the balance heuristic via a novel extended-space representation of the estimator, leading to straightforward annealing schemes for variance reduction purposes. We will also look at the intractable scenario where the proposal density is only available as a joint function with the discrete label, as may be encountered in problems where an ordering is imposed.
- Week 2 - 11th October - cancelled (OxWaSP workshop)
- Week 3 - 18th October - Alice Corbella (Warwick) - "Pseudo Marginal methods in practice: Inferring epidemics from multiple dependent data"
The evolution of an epidemic can be represented as a non-linear dynamical system, combining a transmission model, approximating the unobserved infection process, with further stochastic models for the development and detection of symptoms.
Pseudo Marginal methods (Andrieu and Roberts 2009) provide a useful tool for the inference in dynamic system where the likelihood can be estimated by (sequential) Monte Carlo methods.
We formulate a model for Influenza dynamics in England when multiple, dependent data are available. We also propose a Monte Carlo estimate of the likelihood and use it in a Pseudo Marginal framework. We also present a misspecified model that does not account for dependence between data sources, leading to direct computation of the likelihood. We compare via simulation the inference in the two cases showing that when the dependence is disregarded, this leads to over-efficient estimates.
Lastly, we present an analysis of real data, proving the usefulness of our model and the inference.
- Week 4 - 25th October - Lionel Riou-Durand (Warwick)
- Week 5 - 1st November - Anthony Mulholland (Bristol)
- Week 6 - 8th November - Kathryn Leeming (Warwick)
- Week 7 - 15th November - Nikolas Kantas (Imperial)
- Week 8 - 22nd November - Chris Sherlock (Lancaster)
- Week 9 - 29th November - cancelled (instead, please attend the Dept Colloquium by Terry Lyons (Oxford) in MS.02, 2-3pm on Tuesday 26th November)
- Week 10 - 6th December - Martina Favero (KTH Stockholm)
2019/20 Term 2:
- Week 1 - 10th January - Neil Chada (National University of Singapore)
- Week 2 - 17th January - Alex Buchholz (Cambridge)
- Week 3 - 24th January -
- Week 4 - 31st January -
- Week 5 - 7th February - Ioannis Kosmidis (Warwick)
- Week 6 - 14th February -
- Week 7 - 21st February -
- Week 8 - 28th February -
- Week 9 - 6th March -
- Week 10 - 13th March -
2019/20 Term 3:
- Week 1 - 24th April -
- Week 2 - 1st May -
- Week 3 - 8th May -
- Week 4 - 15th May -
- Week 5 - 22nd May - Helen Ogden (Southampton)
- Week 6 - 29th May -
- Week 7 - 5th June - Ruth Baker (Oxford)
- Week 8 - 12th June -
- Week 9 - 19th June -
- Week 10 - 26th June -
Some key phrases:
- Sampling and inference for diffusions
- Exact algorithms
- Intractable likelihood
- Pseudo-marginal algorithms
- Particle filters
- Importance sampling
- Adaptive MCMC
- Perfect simulation
- Markov chains...
- Random structures...
- Randomised algorithms...