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Handbook of Approximate Bayesian Computation

Edited by: S. A. Sisson , Y. Fan , M. A. Beaumont

Print publication date:  August  2018
Online publication date:  August  2018

Print ISBN: 9781439881507
eBook ISBN: 9781315117195
Adobe ISBN:

10.1201/9781315117195
 Cite  Marc Record

Book description

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.

Table of contents

Prelims Download PDF
Chapter  1:  Overview of ABC Download PDF
Chapter  2:  On the History of ABC Download PDF
Chapter  3:  Regression Approaches for ABC Download PDF
Chapter  4:  ABC Samplers Download PDF
Chapter  5:  Summary Statistics Download PDF
Chapter  6:  Likelihood-Free Model Choice Download PDF
Chapter  7:  ABC and Indirect Inference Download PDF
Chapter  8:  High-Dimensional ABC Download PDF
Chapter  9:  Theoretical and Methodological Aspects of Markov Chain Monte Carlo Computations with Noisy Likelihoods Download PDF
Chapter  10:  Asymptotics of ABC Download PDF
Chapter  11:  Informed Choices: How to Calibrate ABC with Hypothesis Testing Download PDF
Chapter  12:  Approximating the Likelihood in ABC Download PDF
Chapter  13:  A Guide to General-Purpose ABC Software Download PDF
Chapter  14:  Divide and Conquer in ABC Download PDF
Chapter  15:  Sequential Monte Carlo-ABC Methods for Estimation of Stochastic Simulation Models of the Limit Order Book Download PDF
Chapter  16:  Inferences on the Acquisition of Multi-Drug Resistance in Mycobacterium Tuberculosis Using Molecular Epidemiological Data Download PDF
Chapter  17:  ABC in Systems Biology Download PDF
Chapter  18:  Application of ABC to Infer the Genetic History of Pygmy Hunter-Gatherer Populations from Western Central Africa Download PDF
Chapter  19:  ABC for Climate: Dealing with Expensive Simulators Download PDF
Chapter  20:  ABC in Ecological Modelling Download PDF
Chapter  21:  ABC in Nuclear Imaging Download PDF
Index Download PDF
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