Data assimilation with agent-based models using Markov chain sampling

Every day, weather forecasting centres around the world make use of noisy, incomplete observations atmosphere to update their forecasts. This process is known as data assimilation, fusion or state estimation and best expressed Bayesian inference: given a set observations, some prior beliefs model target system, what probability distribution unobserved quantities latent variables at time, possibly in future? While assimilation has developed rapidly areas, relatively little progress been made performing with agent-based models. hampered models quantitative claims about real-world systems. Here we present an algorithm that uses Markov-Chain-Monte-Carlo (MCMC) methods generate samples parameters trajectories over window time aggregated system. can be used as-is, part cycle sequential-MCMC algorithm. Our applicable time-stepping, whose agents have finite states number ways acting on world. As presented, only practical for few bytes internal although discuss removing this restriction. We demonstrate by agent-based, spatial predator-prey model.