2013IRIS Research product catalog (Sapienza University of Rome)Requires access

Approximate Bayesian computation and applications

Clara Grazian

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Abstract

Recent developments allow Bayesian analysis also when the likelihood function\nis intractable, that means it is analytically unavailable or computationally prohibitive to evaluate. These methods are known as “approximate Bayesian computation” (ABC) or likelihood-free methods and are characterized by the fact that the approximation of the posterior distribution is obtained without explicitly evaluating the likelihood function. This kind of analysis is popular in genetic and financial settings. In this work, ABC and some possible applications will be presented.

About this research paper

What this paper is about

Recent developments allow Bayesian analysis also when the likelihood function\nis intractable, that means it is analytically unavailable or computationally prohibitive to evaluate. These methods are known as “approximate Bayesian computation” (ABC) or likelihood-free methods and are characterized by the fact that the approximation of the posterior distribution is obtained without explicitly evaluating the likelihood function. This kind of analysis is popular in genetic and financial settings. In this work, ABC and some possible applications will be presented.

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Method / approach

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Available abstract

Recent developments allow Bayesian analysis also when the likelihood function\nis intractable, that means it is analytically unavailable or computationally prohibitive to evaluate. These methods are known as “approximate Bayesian computation” (ABC) or likelihood-free methods and are characterized by the fact that the approximation of the posterior distribution is obtained without explicitly evaluating the likelihood function. This kind of analysis is popular in genetic and financial settings. In this work, ABC and some possible applications will be presented.

Key concepts: Approximate Bayesian computation, Likelihood function, Bayesian probability, Computer science, Computation, Posterior probability, Marginal likelihood, Algorithm

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