Afternoon meeting on Bayesian Computation
Joint meeting of the RSS Reading Local Group and the University of Reading
19th April 2016, 1300-1800 (provisional times)
University of Reading, Nike Lecture Theatre, Agriculture Building
Speakers:
Prof. Arnaud Doucet, University of Oxford
Prof. Antonietta Mira, Università della Svizzera italiana
others TBA
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 main topic of this meeting.
Contributions:
Along with the two listed speakers, we aim to have several 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 Monday 21st March 2016. 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 to 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. Arnaud Doucet
Title: On a novel class of pseudo-marginal algorithms
Abstract: The pseudo-marginal algorithm is a popular variant of the Metropolis--Hastings scheme which allows us to sample asymptotically from a target probability density when we are only able to estimate unbiasedly an unnormalized version of this target. It has found numerous applications in Bayesian statistics as there are many scenarios where the likelihood function is intractable but can be estimated unbiasedly using Monte Carlo samples. For a fixed computing time, it has been shown in several recent contributions that an efficient implementation of the pseudo-marginal method requires the variance of the log-likelihood ratio estimator appearing in the acceptance probability of the algorithm to be of order 1, which in turn usually requires scaling the number N of Monte Carlo samples linearly with the number T of data points. We propose two novel pseudo-marginal algorithms which are based on low-variance estimators of the log-likelihood ratio appearing in Metropolis-Hastings. We show that the parameters of these schemes can be selected such that the variance of these estimators is of order 1 as $N,T\rightarrow\infty$ whenever $N/T\rightarrow0$; e.g. N=log(T). In our numerical examples, the efficiency of computations is increased relative to the standard pseudo-marginal algorithm by several order of magnitude for large data sets.
Speaker: Prof. Antonietta Mira
Title: TBA
Abstract: TBA
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