Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/231563
Title: BAYESIAN LIKELIHOOD-FREE INFERENCE
Authors: ATLANTA CHAKRABORTY
ORCID iD:   orcid.org/0000-0001-6679-6054
Keywords: Likelihood-free inference, Approximate Bayesian computation, Prior-data conflicts, Modularization, Synthetic likelihood, Model mis-specification
Issue Date: 20-Jun-2022
Citation: ATLANTA CHAKRABORTY (2022-06-20). BAYESIAN LIKELIHOOD-FREE INFERENCE. ScholarBank@NUS Repository.
Abstract: Likelihood-free inference approaches are used when the likelihood is intractable or unavailable in closed form.They are challenging to implement when the model parameter is high-dimensional. These situations arise in a variety of real-world applications ranging from genetics to financial modeling, thus making it crucial to address these challenges. As the first contribution of the thesis, we consider a post-processing adjustment for likelihood-free inference using an optimization based approach. The essence of the approach is to combine estimates from many low- dimensional problems to improve the particle estimate of the posterior in the original high-dimensional problem. Secondly, we develop some new methods for assessing whether the information in the data and the prior are consistent or not in Bayesian inference. We also consider specifying a “weakly-informative” prior for likelihood-free inferences, in situations where a prior-data conflict occurs. Finally, we consider “cutting feedback” approaches which aim to modify Bayesian inference for a model consisting of a number of coupled modules and with a misspecification in any of the modules.
URI: https://scholarbank.nus.edu.sg/handle/10635/231563
Appears in Collections:Ph.D Theses (Open)

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