### Abstract

Language | English |
---|---|

Publication status | Published - 2012 |

Event | MASAMB - Berlin, Germany Duration: 10 Apr 2012 → 11 Apr 2012 |

### Workshop

Workshop | MASAMB |
---|---|

Country | Germany |

City | Berlin |

Period | 10/04/12 → 11/04/12 |

### Fingerprint

### Keywords

- Approximate Bayesian Computation
- probability
- Path Sampling

### Cite this

*Evidential model ranking without likelihoods*. MASAMB, Berlin, Germany.

}

**Evidential model ranking without likelihoods.** / Vyshemirsky, Vladislav.

Research output: Contribution to conference › Speech

TY - CONF

T1 - Evidential model ranking without likelihoods

AU - Vyshemirsky, Vladislav

PY - 2012

Y1 - 2012

N2 - 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.

AB - 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.

KW - Approximate Bayesian Computation

KW - probability

KW - Path Sampling

UR - http://masamb2012.molgen.mpg.de/submission_4.html

M3 - Speech

ER -