CALL FOR CONTRIBUTIONS: Afternoon meeting on Bayesian Computation, 21st January 2015, University of Reading

Afternoon meeting on Bayesian Computation
Joint meeting of the RSS Reading Local Group and the University of Reading
Wednesday 21st January 2015, 1400-1800 (provisional times)
University of Reading, Palmer Building, Room 103

Speakers:
Prof. Christophe Andrieu, University of Bristol
Prof. Christian Robert, Université Paris-Dauphine and University of Warwick
Dr. Richard Everitt, University of Reading
Dr. Dennis Prangle, University of Reading

Description:
The Bayesian approach to statistical inference has seen major successes in the past twenty years, finding application in many areas of science, engineering, finance and elsewhere.  The main drivers of these successes were developments in Monte Carlo methods and the wide availability of desktop computers.  More recently, the use of standard Monte Carlo methods has become infeasible due the size and complexity of data now available.  This has been countered by the development of next-generation Monte Carlo techniques, which are the topic of this meeting.

Contributions:
Along with the four listed speakers, we aim to have a small number of contributed talks, and also a poster session.  If you would like to contribute, please contact Richard Everitt ([log in to unmask]) with a title and abstract by Friday 5th December 2014.  The number of contributed talks and posters will be limited, and you will be informed the following week if we are able to accept your contribution.  The talks and posters are open everyone, including students. We aim to provide a supportive and collaborative environment for you to share your work.

Registration:
The event is open to all, and there is no registration fee.

Directions:
The meeting is on the first floor of the Palmer Building, which is building number 26 on the map at http://goo.gl/AtV6rU.  The university is easily accessed by bus from the railway station (see http://goo.gl/Ybe9AB for further details).  Parking is limited on campus - please contact Richard Everitt ([log in to unmask]) if you require a parking permit.  

Titles and abstracts:

Speaker: Prof. Christophe Andrieu
Title: TBA
Abstract: TBA

Speaker: Prof. Christian Robert
Title: Reliable ABC model choice via random forests
Abstract: Approximate Bayesian computation (ABC) methods provide an elaborate approach to Bayesian inference on complex models, including model choice. Both theoretical arguments and simulation experiments indicate, however, that model posterior probabilities are poorly evaluated by ABC. We propose a novel approach based on a machine learning tool named random forests to conduct selection among the highly complex models covered by ABC algorithms. We strongly shift the way Bayesian model selection is both understood and operated, since we replace the evidential use of model posterior probabilities by predicting the model that best fits the data with random forests and computing an associated posterior error rate. Compared with past implementations of ABC model choice, the ABC random forest approach offers several improvements: (i) it has a larger discriminative power among the competing models, (ii) it is robust to the number and choice of statistics summarizing the data, (iii) the computing effort is drastically reduced, and (iv) it includes an embedded and cost-free error evaluation conditional on the actual analyzed dataset. Random forest will undoubtedly extend the range of size of datasets and complexity of models that ABC can handle. We illustrate the power of the ABC random forest methodology by analyzing controlled experiments as well as real population genetics datasets.

Speaker: Dr. Richard Everitt
Title: Exact and inexact sequential Monte Carlo for inference and model selection in Markov random fields
Abstract: Markov random field models are used widely in computer science, statistical physics and spatial statistics and network analysis.  However, Bayesian analysis of these models using standard Monte Carlo methods is not possible due to an intractable likelihood function.  This talk examines the use of random weight sequential Monte Carlo (SMC) methods for performing parameter inference and evidence estimation in these models and explores the possibility of using importance samplers with biased weights, examining the advantages and drawbacks of this approach.

Speaker: Dr. Dennis Prangle
Title: Lazy ABC
Abstract: Approximate Bayesian computation (ABC) performs approximate statistical inference for otherwise intractable probability models by accepting parameter proposals when corresponding simulated datasets are sufficiently close to the observations. This involves producing a large number of model simulations, which demands considerable computing time. However, it is often clear before a simulation ends that it is unpromising: it is likely to produce a poor match or require excessive time. This talk is on "lazy ABC", an ABC algorithm which saves time by abandoning some such simulations. This is accomplished in such a way as to leave the target distribution unchanged from that of standard ABC i.e. no further approximation is introduced.
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