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

2015 ◽  
Vol 26 (7) ◽  
pp. 451-462 ◽  
Author(s):  
Libo Sun ◽  
Chihoon Lee ◽  
Jennifer A. Hoeting
Keyword(s):  
Model Selection ◽  
Approximate Bayesian Computation ◽  
Bayesian Computation ◽  
Dynamical Models ◽  
Parameter Inference ◽  
Approximate Bayesian

scholarly journals Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems

2008 ◽  
Vol 6 (31) ◽  
pp. 187-202 ◽  
Author(s):  
Tina Toni ◽  
David Welch ◽  
Natalja Strelkowa ◽  
Andreas Ipsen ◽  
Michael P.H Stumpf
Keyword(s):  
Model Selection ◽  
Approximate Bayesian Computation ◽  
Sequential Monte Carlo ◽  
Bayesian Computation ◽  
Parameter Inference ◽  
Credible Intervals ◽  
Computation Scheme ◽  
Abc Method ◽  
Approximate Bayesian ◽  
Better Than

Approximate Bayesian computation (ABC) methods can be used to evaluate posterior distributions without having to calculate likelihoods. In this paper, we discuss and apply an ABC method based on sequential Monte Carlo (SMC) to estimate parameters of dynamical models. We show that ABC SMC provides information about the inferability of parameters and model sensitivity to changes in parameters, and tends to perform better than other ABC approaches. The algorithm is applied to several well-known biological systems, for which parameters and their credible intervals are inferred. Moreover, we develop ABC SMC as a tool for model selection; given a range of different mathematical descriptions, ABC SMC is able to choose the best model using the standard Bayesian model selection apparatus.

scholarly journals GpABC: a Julia package for approximate Bayesian computation with Gaussian process emulation

2019 ◽  
Author(s):  
Evgeny Tankhilevich ◽  
Jonathan Ish-Horowicz ◽  
Tara Hameed ◽  
Elisabeth Roesch ◽  
Istvan Kleijn ◽  
...  
Keyword(s):  
Gaussian Process ◽  
Stochastic Models ◽  
Approximate Bayesian Computation ◽  
Computational Cost ◽  
Bayesian Computation ◽  
Parameter Inference ◽  
Likelihood Functions ◽  
Gaussian Process Emulation ◽  
Selection For ◽  
Approximate Bayesian

ABSTRACTApproximate Bayesian computation (ABC) is an important framework within which to infer the structure and parameters of a systems biology model. It is especially suitable for biological systems with stochastic and nonlinear dynamics, for which the likelihood functions are intractable. However, the associated computational cost often limits ABC to models that are relatively quick to simulate in practice. We here present a Julia package, GpABC, that implements parameter inference and model selection for deterministic or stochastic models using i) standard rejection ABC or ABC-SMC, or ii) ABC with Gaussian process emulation. The latter significantly reduces the computational cost.URL: https://github.com/tanhevg/GpABC.jl

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