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2015 ; 47
(10
): 176-183
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Simulation-based Bayesian Analysis of Complex Data
#MMPMID27840859
Marjoram P
; Hamblin S
; Foley B
Summer Comput Simul Conf (2015)
2015[Dec]; 47
(10
): 176-183
PMID27840859
show ga
Our ability to collect large datasets is growing rapidly. Such richness of data
offers great promise in terms of addressing detailed scientific questions in
great depth. However, this benefit is not without scientific difficulty: many
traditional analysis methods become computationally intractable for very large
datasets. However, one can frequently still simulate data from scientific models
for which direct calculation is no longer possible. In this paper we propose a
Bayesian perspective for such analyses, and argue for the advantage of a
simulation-based approximate Bayesian method that remains tractable when
tractability of other methods is lost. This method, which is known as
"approximate Bayesian computation" [ABC], has now been used in a variety of
contexts, such as the analysis of tumor data (a tumor being a complex population
of cells), and the analysis of human genetic variation data (which arise from a
population of individual people). We review a number of ABC methods, with
specific attention to the use of ABC in agent-based models, and give pointers to
software that allows straightforward implementation of the ABC approach. In this
way we demonstrate the utility of simulation-based analyses of large datasets
within a rigorous statistical framework.
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