Further to this, the meeting has a website at:
On 22 Oct 2013, at 13:13, Richard Everitt <[log in to unmask]> wrote:
> Afternoon meeting on Bayesian Computation
> Joint meeting of the RSS Reading Local Group and the University of Reading
> Friday 6th December 1400-1800 (provisional times)
> University of Reading, Palmer Building, Room 107
> Prof. Mark Beaumont, University of Bristol
> Prof. Mark Girolami, UCL
> Dr. Richard Everitt, University of Reading
> Dr. Dennis Prangle, University of Reading
> 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.
> Along with the four invited 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 8th November 2013. 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
> The event is open to all, and there is no registration fee.
> 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. Mark Beaumont
> Title: Detecting selection with approximate Bayesian computation
> Abstract: In population genetics the parameters describing genetic variation at each site in the genome can often be regarded as conditionally independent, with, for example, mutation rate or selection coefficients drawn from some common distribution that we wish to characterise. This hierarchical structure is potentially problematic for inference based on Approximate Bayesian computation (ABC), because one may be interested in both the hyper-parameters and also the parameters describing, for example, each of many loci, requiring a large number of summary statistics. A general method is described for addressing these problems efficiently, and is applied to detect natural selection in the genome.
> Speaker: Prof. Mark Girolami
> Title: Probabilistic Integration of Differential Equations for Exact Bayesian Uncertainty Quantification
> Abstract: Taking a Bayesian approach to inverse problems is challenging. This talk presents general methodology to explicitly characterize the mismatch between the finite-dimensional approximation of the forward model and the infinite-dimensional solution by a well-defined probability measure in Hilbert space. Furthermore this measure provides a means of obtaining probabilistic solutions of the forward model that resolves issues related to characterising uncertainty in the solutions of differential equations including chaotic systems and the multiplicity of solutions in e.g. boundary value problems. The probabilistic solutions are employed in the quantification of input uncertainty thus resolving problems with optimistic estimates of system uncertainty when solving the inverse problem.
> Speaker: Dr. Richard Everitt
> Title: Likelihood-free inference for 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. Several methods have been developed that permit exact, or close to exact, simulation from the posterior distribution, but each of these methods can be computationally expensive. We explore an alternative approach, which may be of particular use for big data applications.
> Speaker: Dr. Dennis Prangle
> Title: Speeding ABC inference using early-stopping simulations
> Abstract: In fields such as biology and social sciences, dealing with large modern datasets often requires complicated models whose likelihood functions cannot easily be numerical evaluated. This makes statistical inference of the model parameters difficult by standard methods. There has been much recent study of "likelihood-free" approaches which favour parameter values for which simulated data from the model is similar to the observed data. This talk concentrates on Approximate Bayesian computation (ABC), which put this approach into a Bayesian framework.
> A bottleneck in likelihood-free methods is the time taken to run simulations from the model. This talk considers the strategy of stopping simulations early if it looks unlikely that they will produce a good match to the observations. An ABC algorithm is proposed which does this without altering the target distribution. Results on how to tune the algorithm and practical examples are also presented.
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