## Abstract

Population extinction is one of the central themes in population biology. We propose a statistical algorithm for long-term prediction of an extinction event in the paradigmatic predator–prey model. The algorithm is based on noisy and sporadic observations of the Lotka–Volterra (LV) system at the early stages of its evolution, when the system is still very far from extinction. There are two stages in the algorithm: first, the unknown parameters (reaction rates) of the LV system are estimated using the Approximate Bayesian Computation method; then an analytical expression for the time-scale of extinction (which involves the estimated parameters) is applied to compute the probability density function of extinction time. The proposed algorithm is validated by numerical simulations for the case of a stochastic LV system specified by the birth–death rate equations. The algorithm can be seen as an initial step in the quest for long-term prediction of rare “catastrophic” events in complex stochastic dynamic systems (epidemics, host-parasite dynamics, enzyme kinetics, dynamic trading, etc.).

Original language | English (US) |
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Pages (from-to) | 371-379 |

Number of pages | 9 |

Journal | Applied Mathematics and Computation |

Volume | 320 |

DOIs | |

State | Published - Mar 1 2018 |

### Bibliographical note

Funding Information:A.K. was supported by NSF Grant DMR1306734 . Appendix

## Keywords

- Bayesian estimation
- Lotka–Volterra system
- Population biology