TY - JOUR
T1 - Parameter inference and model selection in deterministic and stochastic dynamical models via approximate Bayesian computation
T2 - Modeling a wildlife epidemic
AU - Sun, Libo
AU - Lee, Chihoon
AU - Hoeting, Jennifer A.
N1 - Publisher Copyright:
© 2015 John Wiley & Sons, Ltd.
PY - 2015/11
Y1 - 2015/11
N2 - 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.
AB - 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.
KW - Approximate Bayesian computation
KW - Chronic wasting disease
KW - Continuous-time Markov chain
KW - Model selection
KW - Ordinary and stochastic differential equations
KW - Parameter inference
UR - http://www.scopus.com/inward/record.url?scp=84945460926&partnerID=8YFLogxK
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U2 - 10.1002/env.2353
DO - 10.1002/env.2353
M3 - Article
AN - SCOPUS:84945460926
SN - 1180-4009
VL - 26
SP - 451
EP - 462
JO - Environmetrics
JF - Environmetrics
IS - 7
ER -