About the Project
Approximate Bayesian computation (ABC) is a paradigm which allows one to perform Bayesian statistical inference, when the associate probability is totally intractable in a certain manner. In order to conduct this afore-mentioned inference, one must resort to Monte Carlo estimation.
A/P Jasra and Dr Jo are currently developing a method which allows Monte Carlo estimation in the context of ABC, which permits a reduction in computational effort, for a given level of error in the Monte Carlo estimate. Their methodology relies upon the important multilevel Monte Carlo method coupled with advanced sequential Monte Carlo approximation techniques.
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Related Publications
Ajay Jasra, Seongil Jo, David Nott, Christine Shoemaker, Raul Tempone
In the following article we consider approximate Bayesian computation (ABC) inference. We introduce a method for numerically approximating ABC posteriors using the multilevel Monte Carlo (MLMC). A sequential Monte Carlo version of the approach is developed and it is shown under some assumptions that for a given level of…
Ajay Jasra, Seongil Jo, David Nott, Christine Shoemaker, Raul Tempone
In the following article we consider approximate Bayesian computation (ABC) inference. We introduce a method for numerically approximating ABC posteriors using the multilevel Monte Carlo (MLMC). A sequential Monte Carlo version of the approach is developed and it is shown under some assumptions that for a given level of…
Stochastic Analysis and Applications
