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• W2809916002 abstract "In this work we illustrate some of the challenges posed by highly non-linear stochastic models to statistical inference. In particular, we demonstrate how the likelihood function, which is not directly available when hidden states are present, can become highly multimodal under near-chaotic dynamics and low process noise. We identify two classes of methods that are relevant in this context: the first includes Particle Markov Chain Monte Carlo, Iterated Filtering and Parameters Cascading, the second Approximate Bayesian Computation and Synthetic Likelihood. After showing that the two groups are differently affected by the issue of multimodality, we compare their performance on a group of simple stochastic maps. We then select Particle Marginal Metropolis Hastings and Synthetic likelihood as representatives of each class, and we compare them on two complex models, using both real and simulated datasets. The result of the comparison is a cautionary tale, suggesting the joint use of both approaches in order to achieve robust conclusions." @default.
• W2809916002 created "2018-07-10" @default.
• W2809916002 creator A5017828577 @default.
• W2809916002 creator A5053318488 @default.
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• W2809916002 date "2014-11-17" @default.
• W2809916002 modified "2023-09-27" @default.
• W2809916002 title "Statistical inference for highly non-linear dynamical models in ecology and epidemiology" @default.
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• W2809916002 hasPublicationYear "2014" @default.
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