As the world becomes increasingly complex, so do the statistical
models required to analyse the challenging problems ahead. For the
very first time in a single volume, the Handbook of Approximate
Bayesian Computation (ABC) presents an extensive overview of the
theory, practice and application of ABC methods. These simple, but
powerful statistical techniques, take Bayesian statistics beyond
the need to specify overly simplified models, to the setting where
the model is defined only as a process that generates data. This
process can be arbitrarily complex, to the point where standard
Bayesian techniques based on working with tractable likelihood
functions would not be viable. ABC methods finesse the problem of
model complexity within the Bayesian framework by exploiting modern
computational power, thereby permitting approximate Bayesian
analyses of models that would otherwise be impossible to implement.
The Handbook of ABC provides illuminating insight into the world of
Bayesian modelling for intractable models for both experts and
newcomers alike. It is an essential reference book for anyone
interested in learning about and implementing ABC techniques to
analyse complex models in the modern world.
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