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.
|Publication status||Published - 2012|
|Event||MASAMB - Berlin, Germany|
Duration: 10 Apr 2012 → 11 Apr 2012
|Period||10/04/12 → 11/04/12|
- Approximate Bayesian Computation
- Path Sampling