1st Edition
Handbook of Approximate Bayesian Computation
Introduction
Overview of approximate Bayesian computation: S. A. Sisson, Y. Fan and M. A. Beaumont
On the history of ABC: S.Tavare
Regression approaches: M. G. B. Blum
Monte Carlo samplers for ABC: Y. Fan and S. A. Sisson
Summary statistics: D. Prangle
Likelihood-free model choose: 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 MCMC computations with noisy likelihoods: C. Andrieu, A.Lee and M. Viola
Informed Choices: How to calibrate ABC with hypothesis testing: O. Ratmann, A. Camacho, S. Hu and C. Coljin
Approximating the likelihood in approximate Bayesian computation: C. C. Drovandi, C. Grazian, K. Mengersen and C. Robert
Software: D.Wegmann
Divide and conquer in ABC: Expectation-Propagation algorithms for likelihood-free inference: S. Barthelme, N. Chopin and V. Cottet
SMC-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, M. M. Tanaka
ABC in Systems Biology: J. Liepe and M. P. H. Stumpf
Application of approximate Bayesian computation to make inference about the genetic history of Pygmy hunter-gatherers populations from Western Central Africa: A. Estoup et al
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
Biography
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.
"The Handbook of Approximate Bayesian Computation presents basic approaches as well as extension and mathematical details about ABC approaches. Advantages (simplicity, wide applicability) as well as challenges (computational burden, various assumptions/choice of tuning parameters) of ABC are discussed in theory and application ... the Handbook of Approximate Bayesian Computation is an excellent book and an indispensable choice for all (beginners and advanced users) who are interested in obtaining a deeper understanding of ABC approaches in application as well as statistical theory."
-Heiko Götte, Merck Healthcare KGaA, Darmstadt, Germany
