Parameter inference and model selection in deterministic and stochastic dynamical models via approximate Bayesian computation: Modeling a wildlife epidemic

Libo Sun, Chihoon Lee, Jennifer A. Hoeting

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

Abstract

We consider the problem of selecting deterministic or stochastic models for a biological, ecological, or environmental dynamical process. In most cases, one prefers either deterministic or stochastic models as candidate models based on experience or subjective judgment. Because of the complex or intractable likelihood in most dynamical models, likelihood-based approaches for model selection are not suitable. We use approximate Bayesian computation for parameter estimation and model selection to gain further understanding of the dynamics of two epidemics of chronic wasting disease in mule deer. The main novel contribution of this work is that, under a hierarchical model framework, we compare three types of dynamical models: ordinary differential equation, continuous-time Markov chain, and stochastic differential equation models. To our knowledge, model selection between these types of models has not appeared previously. Because the practice of incorporating dynamical models into data models is becoming more common, the proposed approach may be very useful in a variety of applications.

Original languageEnglish
Pages (from-to)451-462
Number of pages12
JournalEnvironmetrics
Volume26
Issue number7
DOIs
StatePublished - Nov 2015

Keywords

  • Approximate Bayesian computation
  • Chronic wasting disease
  • Continuous-time Markov chain
  • Model selection
  • Ordinary and stochastic differential equations
  • Parameter inference

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