Bayesian parameter inference for individual-based models using a Particle Markov Chain Monte Carlo method
Authored by Mira Kattwinkel, Peter Reichert
Date Published: 2017
DOI: 10.1016/j.envsoft.2016.11.001
Sponsors:
No sponsors listed
Platforms:
Java
R
Model Documentation:
ODD
Flow charts
Mathematical description
Model Code URLs:
http://www.sciencedirect.com.ezproxy1.lib.asu.edu/science/MiamiMultiMediaURL/1-s2.0-S1364815216308908/1-s2.0-S1364815216308908-mmc1.zip/271872/html/S1364815216308908/3cb8929244157644dbe1b93be30e3bd3/mmc1.zip
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. (C) 2016 Elsevier
Ltd. All rights reserved.
Tags
Agent-based models
Dynamics
Parameter estimation
calibration
ecology
decision-support
Agent-based model (ABM)
Computation
Bayesian inference
Populations
Protocol
State-space models
Individual-based model (ibm)
Particle markov chain
monte carlo (pmcmc)
Approximate bayesian computation (abc)