Evidential model ranking without likelihoods
Vyshemirsky, Vladislav (2012) Evidential model ranking without likelihoods. In: MASAMB, 2012-04-10 - 2012-04-11. (http://masamb2012.molgen.mpg.de/submission_4.html)
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We present a probabilistic formulation of the Approximate Bayesian Computation scheme that allows evidential ranking of alternative models without direct use of a likelihood function. This approach is particularly important when ranking of several sophisticated stochastic models is desired, and the likelihood is either too complex or impossible to define. We suggest a modification of a Sequential Monte-Carlo sampler that uses ideas of Path Sampling to estimate an approximation to marginal likelihoods as a measure of evidence support. We demonstrate applications of this method on a problem of ranking alternative models of cancerous tumour growth using unique data from three cancerous spheroid lines.
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Item type: Conference or Workshop Item(Speech) ID code: 41461 Dates: DateEvent2012PublishedKeywords: Approximate Bayesian Computation , probability, Path Sampling, Probabilities. Mathematical statistics, SDG 3 - Good Health and Well-being Subjects: Science > Mathematics > Probabilities. Mathematical statistics Department: Faculty of Science > Mathematics and Statistics Depositing user: Pure Administrator Date deposited: 15 Oct 2012 14:00 Last modified: 18 Jan 2023 13:09 URI: https://strathprints.strath.ac.uk/id/eprint/41461