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Royal Statistical Society |
| 1998 | |
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5.00 p.m., 11 November |
STEPHEN G WALKER (Imperial College, London), PAUL DAMIEN (University of Michigan, USA),
PURUSHOTTAM W LAUD (Medical College of Wisconsin, USA) &
ADRIAN F M SMITH (Queen Mary and Westfield College, London)
Bayesian nonparametric inference for random distributions and related functions In recent years, Bayesian nonparametric inference, both theoretical and computational has witnessed considerable advances. However, these advances have not received a full critical and comparative analysis of their scope, impact and limitations in statistical modelling; many aspects of the theory and methods remain a mystery to practitioners and many open questions remain. In this paper, we discuss and illustrate the rich modelling and analytic possibilities available to the statistician within the Bayesian nonparametric and/or semiparametric framework. Appears (with discussion) in Journal of the Royal Statistical Society, Series B, 61(3), 485-527 (1999). |
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5.00 p.m., 9 December |
JULIAN BESAG (University of Washington, USA) and DAVID HIGDON (Duke University, USA)
Bayesian analysis of agricultural field experiments The paper describes Bayesian analysis for agricultural field experiments, a topic that has received very little previous attention, despite a vast frequentist literature. Adoption of the Bayesian paradigm simplifies the interpretation of the results, especially in ranking and selection. Also, complex formulations can be analyzed with comparative ease using Markov chain Monte Carlo methods. A key ingredient in the approach is the need for spatial representations of the observed fertility patterns. This is discussed in detail. Problems caused by outliers and by jumps in fertility are tackled via hierarchical-t formulations that may find use in other contexts. The paper includes three analyses of variety trials for yield and one example involving binary data: none is entirely straightforward. Some comparisons with frequentist analyses are made. Appears (with discussion) in Journal of the Royal Statistical Society, Series B, 61(4), 691-746 (1999). |
| 1999 | |
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5.00 p.m., 10 February |
MARCUS DACRE, KEVIN GLAZEBROOK (University of Newcastle)
& JOSE NINO-MORA (Universitat Pompeu Fabra, Spain)
The achievable region approach to the optimal control of stochastic systems The last decade has seen a major research focus on the optimisation of complex stochastic service systems, motivated in part by applications to computer and telecommunications networks. The achievable region approach to such problems is radically different from conventional formulations based on dynamic programming. The paper illustrates the approach by a simple example and then describes some important recent advances. Appears (with discussion) in Journal of the Royal Statistical Society, Series B, 61(4), 747-791 (1999). |
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10.30am - 5.30pm, 23 March |
Modern approaches to analysis of genetic data
This was an expository meeting, with several invited speakers:
David Clayton (MRC Cambridge): "Disequilibrium mapping"
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5.00 p.m., 19 May |
JIM DURBIN (London School of Economics)
& SIEM JAN KOOPMAN (Tilburg University, Netherlands)
Time series analysis of non-Gaussian observations based on state space models from both classical and Bayesian perspectives. The analysis of non-Gaussian time series using state space models is considered from both classical and Bayesian perspectives. The treatment in both cases is based on simulation using importance sampling and antithetic variables. The techniques are illustrated by applying them to a discrete series, a series with outliers and a volatility series. Appears (with discussion) in Journal of the Royal Statistical Society, Series B, 62(1), 3-56 (2000). |