Handbook of Approximate Bayesian Computation

1. Auflage 2018
ISBN: 978-1-4398-8150-7
Verlag: Chapman and Hall/CRC

Buch, Englisch, 678 Seiten, Format (B × H): 161 mm x 240 mm, Gewicht: 1174 g

Reihe: Chapman & Hall/CRC Handbooks of Modern Statistical Methods

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.

Autoren/Hrsg.

Sisson, Scott A.

Fan, Yanan

Beaumont, Mark

Fachgebiete

• Mathematik | Informatik Mathematik Stochastik

Inhaltsverzeichnis

Introduction
Overview of ABC: S. A. Sisson, Y. Fan and M. A. Beaumont
On the history of ABC: S.Tavare
Regression approaches: M. G. B. Blum
ABC Samplers: S. A. Sisson and Y. Fan

Summary statistics: D. Prangle
Likelihood-free Model Choice: J.-M. Marin, P. Pudlo, A. Estoup and C. Robert
ABC and Indirect Inference: C. C. Drovandi
High-Dimensional ABC: D. Nott, V. Ong, Y. Fan and S. A. Sisson

Theoretical and Methodological Aspects of Markov Chain Monte CarloComputations with Noisy Likelihoods: C. Andrieu, A.Lee and M. Viola
Asymptotics of ABC: Paul Fearnhead
Informed Choices: How to Calibrate ABC with Hypothesis Testing: O. Ratmann, A. Camacho, S. Hu and C. Coljin
Approximating the Likelihood in ABC: C. C. Drovandi, C. Grazian, K. Mengersen and C. Robert
Divide and Conquer in ABC: Expectation-Propagation algorithms for likelihood-free inference: S. Barthelme, N. Chopin and V. Cottet
Sequential Monte Carlo-ABC Methods for Estimation of Stochastic Simulation Models of the Limit Order Book: G. W. Peters, E. Panayi and F. Septier
Inferences on the Acquisition of Multidrug Resistance in Mycobacterium Tuberculosis Using Molecular Epidemiological Data: G. S. Rodrigues, S. A. Sisson, andM. M. Tanaka
ABC in Systems Biology: J. Liepe and M. P. H. Stumpf
Application ofABC toInfer about the Genetic History of Pygmy Hunter-Gatherers Populations from Western Central Africa: A. Estoup, P. Verdu, J.-M. Marin, C. Robert, A. Dehne-Garcia, J.-M. Cornuet andP. Pudlo
ABC for Climate: Dealing with Expensive Simulators: P. B. Holden, N. R. Edwards, J. Hensman and R. D. Wilkinson
ABC in Ecological Modelling: M. Fasiolo and S. N. Wood
ABC in Nuclear Imaging: Y. Fan, S. R. Meikle, G. Angelis and A. Sitek

Über Autor(innen)

Scott Sission is Professor, ARC Future Fellow and Head of Statistics in the School of Mathematics and Statistics at UNSW.

Yanan Fan is a Senior Lecturer at the School of Mathematics and Statistics at UNSW.

Mark Beaumont is Professor of Statistics at the University of Bristol.