Evidential model ranking without likelihoods

Vladislav Vyshemirsky

Research output: Contribution to conferenceSpeech


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.
Original languageEnglish
Publication statusPublished - 2012
EventMASAMB - Berlin, Germany
Duration: 10 Apr 201211 Apr 2012




  • Approximate Bayesian Computation
  • probability
  • Path Sampling


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