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- W40036031 abstract "We have a probabilistic statistical model which is required to adapt in the light ofobserved cases. The adapted model can be viewed as the knowledge base of an expertsystem which is designed to solve a complex forensic problem. The adaptation takesthe form of Bayesian parameter learning and since the data is incomplete we have ananalytically intractable problem that requires some form of approximation. In thisthesis the chosen form is Gibbs sampling.We categorise the various forms of Gibbs sampling as either numerical (in thesense that numerical processing is integral to the algorithm) or algebraic (where thenumerical processing is viewed as a set-up phase). These categories are further subdividedinto complex (where we deal with complex mixtures) and simple methods(where standard conjugate analysis is performed).We show, through computer experiments, that when taking a complex numericalapproach, there is the possibility that reducing the configuration space of the Gibbssampler can compensate for the computational inefficiency of performing the Gibbsiterations. Thus there is the possibility that, for certain types of problems, thisapproach will out-perform the commonly used simple numerical method. A simplealgebraic approach is developed which aims to improve efficiency by reducing thetime taken for a single iteration of the sampler. An example is introduced, namelythe mutation rate learning problem, to demonstrate the application of this method.We conclude that in certain circumstances it may be reasonable to compromise onaccuracy in order to take advantage of the efficiency of the above methods." @default.
- W40036031 created "2016-06-24" @default.
- W40036031 creator A5016850814 @default.
- W40036031 date "2011-09-28" @default.
- W40036031 modified "2023-10-18" @default.
- W40036031 title "Gibbs sampling for parameter learning in probabilistic expert systems" @default.
- W40036031 hasPublicationYear "2011" @default.
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