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

SPUX: Scalable Particle Markov Chain Monte Carlo for uncertainty quantification in stochastic ecological models