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Sammanfattning
A maximum likelihood methodology for the parameters of models with an intractable likelihood is introduced. We produce a likelihoodfree version of the stochastic approximation expectationmaximization (SAEM) algorithm to maximize the likelihood function of model parameters. While SAEM is best suited for models having a tractable "complete likelihood" function, its application to moderately complex models is a difficult or even impossible task. We show how to construct a likelihoodfree version of SAEM by using the "synthetic likelihood" paradigm. Our method is completely plugandplay, requires almost no tuning and can be applied to both static and dynamic models. Four simulation studies illustrate the method, including a stochastic differential equation model, a stochastic LotkaVolterra model and data from gandk distributions. MATLAB code is available as supplementary material.
Originalspråk  engelska 

Sidor (fråntill)  861881 
Antal sidor  26 
Tidskrift  Communications in Statistics: Simulation and Computation 
Volym  48 
Nummer  3 
Tidigt onlinedatum  2018 jan. 18 
DOI  
Status  Published  2019 
Ämnesklassifikation (UKÄ)
 Sannolikhetsteori och statistik
Fingeravtryck
Utforska forskningsämnen för ”Likelihoodfree stochastic approximation EM for inference in complex models”. Tillsammans bildar de ett unikt fingeravtryck.Projekt
 1 Aktiva

Stochastic modelling of protein folding and likelihoodfree statistical inference methods
Picchini, U., Forman, J., LindorffLarsen, K. & Wiqvist, S.
2015/01/01 → …
Projekt: Forskning