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SPUX: Scalable Particle Markov Chain Monte Carlo for uncertainty quantification in stochastic ecological models
[article]
2017
arXiv
pre-print
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)
arXiv:1711.01410v1
fatcat:faivqnrkzjhgtf33j4raefxcgi