TY - JOUR
T1 - Approximate Bayesian Computation
A1 - SunnÃ¥ker, Mikael
A1 - Busetto, Alberto Giovanni
A1 - Numminen, Elina
A1 - Corander, Jukka
A1 - Foll, Matthieu
A1 - Dessimoz, Christophe
Y1 - 2013/01/10
N2 - Approximate Bayesian computation (ABC) constitutes a class of computational methods rooted in Bayesian statistics. In all model-based statistical inference, the likelihood function is of central importance, since it expresses the probability of the observed data under a particular statistical model, and thus quantifies the support data lend to particular values of parameters and to choices among different models. For simple models, an analytical formula for the likelihood function can typically be derived. However, for more complex models, an analytical formula might be elusive or the likelihood function might be computationally very costly to evaluate. ABC methods bypass the evaluation of the likelihood function. In this way, ABC methods widen the realm of models for which statistical inference can be considered. ABC methods are mathematically well-founded, but they inevitably make assumptions and approximations whose impact needs to be carefully assessed. Furthermore, the wider application domain of ABC exacerbates the challenges of parameter estimation and model selection. ABC has rapidly gained popularity over the last years and in particular for the analysis of complex problems arising in biological sciences (e.g., in population genetics, ecology, epidemiology, and systems biology).
JF - PLOS Computational Biology
JA - PLOS Computational Biology
VL - 9
IS - 1
UR - https://doi.org/10.1371/journal.pcbi.1002803
SP - e1002803
EP -
PB - Public Library of Science
M3 - doi:10.1371/journal.pcbi.1002803
ER -