Complex models often have intractable likelihoods, so methods that involve evaluation of the likelihood function are infeasible. The aims of the research are • to provide a review of the likelihood free methods (e.g., ABC or synthetic likelihood) used in fitting complex models large dataset; • to use likelihood free methods to make inference on complex models such as random networks models; • to develop the code for the analysis; • to apply the model and methods for networks data from economics and finance such as trade, financial flows networks, financial contagion networks; • to write a final report where methods and results are presented and discussed.

Likelihood free methods for inference on complex models

Facchinetti, Alessandro
2020/2021

Abstract

Complex models often have intractable likelihoods, so methods that involve evaluation of the likelihood function are infeasible. The aims of the research are • to provide a review of the likelihood free methods (e.g., ABC or synthetic likelihood) used in fitting complex models large dataset; • to use likelihood free methods to make inference on complex models such as random networks models; • to develop the code for the analysis; • to apply the model and methods for networks data from economics and finance such as trade, financial flows networks, financial contagion networks; • to write a final report where methods and results are presented and discussed.
2020-03-06
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14247/4858