Vyshemirsky, Vladislav
(2012)
*Evidential model ranking without likelihoods.*
In: MASAMB, 2012-04-10 - 2012-04-11, Berlin.

## Abstract

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.

Item type: | Conference or Workshop Item (Speech) |
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ID code: | 41461 |

Keywords: | Approximate Bayesian Computation , probability, Path Sampling, Probabilities. Mathematical statistics |

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: | 06 Sep 2014 19:34 |

URI: | http://strathprints.strath.ac.uk/id/eprint/41461 |

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