Approximate Bayesian computation and applications
Clara Grazian
Abstract
Clara Grazian
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.
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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