Bayesian parameter inference for individual-based models using a Particle Markov Chain Monte Carlo method. (January 2017) - Kattwinkel, Mira; Reichert, Peter
Abstract: Parameter estimation for agent-based and individual-based models (ABMs/IBMs) is often performed by manual tuning and model uncertainty assessment is often ignored. Bayesian inference can jointly address these issues. However, due to high computational requirements of these models and technical difficulties in applying Bayesian inference to stochastic models, the exploration of its application to ABMs/IBMs has just started. We demonstrate the feasibility of Bayesian inference for ABMs/IBMs with a Particle Markov Chain Monte Carlo (PMCMC) algorithm developed for state-space models. The algorithm profits from the model's hidden Markov structure by jointly estimating system states and the marginal likelihood of the parameters using time-series observations. The PMCMC algorithm performed well when tested on a simple predator-prey IBM using artificial observation data. Hence, it offers the possibility for Bayesian inference for ABMs/IBMs. This can yield additional insights into model behaviour and uncertainty and extend the usefulness of ABMs/IBMs in ecological and environmental research. Graphical abstract: Highlights: We present a new option for Bayesian inference for agent-/individual-based models. The implementation is based on Particle Markov Chain Monte Carlo (PMCMC). We found good performance and insights into model behaviour for a simple IBM with artificial data. Bayesian inference for AMBs/IBMs stimulates mechanistic ecological research.