  Calibration of individual based models (IBMs), successful in modeling complex
ecological dynamical systems, is often performed only ad-hoc. Bayesian
inference can be used for both parameter estimation and uncertainty
quantification, but its successful application to realistic scenarios has been
hindered by the complex stochastic nature of IBMs. Computationally expensive
techniques such as Particle Filter (PF) provide marginal likelihood estimates,
where multiple model simulations (particles) are required to get a sample from
the state distribution conditional on the observed data. Particle ensembles are
re-sampled at each data observation time, requiring particle destruction and
replication, which lead to an increase in algorithmic complexity. We present
SPUX, a Python implementation of parallel Particle Markov Chain Monte Carlo
(PMCMC) algorithm, which mitigates high computational costs by distributing
particles over multiple computational units. Adaptive load re-balancing
techniques are used to mitigate computational work imbalances introduced by
re-sampling. Framework performance is investigated and significant speed-ups
are observed for a simple predator-prey IBM model.
