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On optimality of kernels for approximate Bayesian computation using sequential Monte Carlo.

STATISTICAL APPLICATIONS IN GENETICS AND MOLECULAR BIOLOGY, no. 1 (2013): 87-107

Cited: 53|Views28
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Abstract

Approximate Bayesian computation (ABC) has gained popularity over the past few years for the analysis of complex models arising in population genetics, epidemiology and system biology. Sequential Monte Carlo (SMC) approaches have become work-horses in ABC. Here we discuss how to construct the perturbation kernels that are required in ABC ...More

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