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Bayesian Analysis papers

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Local computation of influence propagation through Bayes linear belief networks
In recent years there has been interest in the theory of local computation over probabilistic Bayesian graphical models. In this paper, local computation over Bayes linear belief networks is shown to…
Darren J WilkinsonSep 2, 1996
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Bayes linear variance adjustment for time series
This paper exhibits quadratic products of linear combinations of observables which identify the covariance structure underlying the univariate locally linear time series dynamic linear model. The…
Darren J WilkinsonApr 2, 1996
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Toward general solutions to time-series problems: Notes on obstacles and noise
Computational difficulties in the general application of Bretthorsts formalism to time-series problems, posed by the large number of possible models and the use of models with nonorthogonal…
Iftah GideoniJan 7, 1996
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Bayes linear covariance matrix adjustment
In this thesis, a Bayes linear methodology for the adjustment of covariance matrices is presented and discussed. A geometric framework for quantifying uncertainties about covariance matrices is set…
Darren J WilkinsonDec 4, 1995
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Bayesian Method of Moments (BMOM) Analysis of Mean and Regression Models
A Bayesian method of moments/instrumental variable (BMOM/IV) approach is developed and applied in the analysis of the important mean and multiple regression models. Given a single set of data, it is…
Arnold ZellnerNov 30, 1995
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Bayesian Variable Selection with Related Predictors
In data sets with many predictors, algorithms for identifying a good subset of predictors are often used. Most such algorithms do not account for any relationships between predictors. For example,…
Hugh ChipmanOct 30, 1995
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Suppressing Random Walks in Markov Chain Monte Carlo Using Ordered Overrelaxation
Markov chain Monte Carlo methods such as Gibbs sampling and simple forms of the Metropolis algorithm typically move about the distribution being sampled via a random walk. For the complex,…
R. M. NealJun 22, 1995
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Minimal information in velocity space
Jaynes' transformation group principle is used to derive the objective prior for the velocity of a non-zero rest-mass particle. In the case of classical mechanics, invariance under the classical…
Guillaume EvrardJun 14, 1995
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Bayes linear covariance matrix adjustment for multivariate dynamic linear models
A methodology is developed for the adjustment of the covariance matrices underlying a multivariate constant time series dynamic linear model. The covariance matrices are embedded in a…
Darren J Wilkinson, Michael GoldsteinJun 5, 1995
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Bayes linear adjustment for variance matrices
We examine the problem of covariance belief revision using a geometric approach. We exhibit an inner-product space where covariance matrices live naturally --- a space of random real symmetric…
Darren J Wilkinson, Michael GoldsteinJun 4, 1995
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Confidence Intervals from One One Observation
Robert Machol's surprising result, that from a single observation it is possible to have finite length confidence intervals for the parameters of location-scale models, is re-produced and…
Carlos C. RodriguezApr 12, 1995
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