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

• IntroductionOverview of approximate Bayesian computation: S. A. Sisson, Y. Fan and M. A. BeaumontOn the history of ABC: S.TavareRegression approaches: M. G. B. BlumMonte Carlo samplers for ABC: Y. Fan and S. A. SissonSummary statistics: D. PrangleLikelihood-free model choose: J.-M. Marin, P. Pudlo, A. Estoup and C. RobertABC and indirect inference: C. C. DrovandiHigh-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. ViolaInformed Choices: How to calibrate ABC with hypothesis testing: O. Ratmann, A. Camacho, S. Hu and C. ColjinApproximating the likelihood in approximate Bayesian computation: C. C. Drovandi, C. Grazian, K. Mengersen and C. RobertSoftware: D.WegmannDivide and conquer in ABC: Expectation-Propagation algorithms for likelihood-free inference: S. Barthelme, N. Chopin and V. CottetSMC-ABC methods for estimation of stochastic simulation models of the limit order book: G.W. Peters, E. Panayi and F. SeptierInferences on the acquisition of multidrug resistance in Mycobacterium tuberculosis using molecular epidemiological data: G. S. Rodrigues, S. A. Sisson, M. M. TanakaABC in Systems Biology: J. Liepe and M. P. H. StumpfApplication of approximate Bayesian computation to make inference about the genetic history of Pygmy hunter-gatherers populations from Western Central Africa: A. Estoup et alABC for climate: dealing with expensive simulators: P. B. Holden, N. R. Edwards, J. Hensman and R. D. WilkinsonABC in ecological modelling: M. Fasiolo and S. N. WoodABC in Nuclear Imaging: Y. Fan, S. R. Meikle, G. Angelis and A. Sitek

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

• IntroductionOverview of approximate Bayesian computation: S. A. Sisson, Y. Fan and M. A. BeaumontOn the history of ABC: S.TavareRegression approaches: M. G. B. BlumMonte Carlo samplers for ABC: Y. Fan and S. A. SissonSummary statistics: D. PrangleLikelihood-free model choose: J.-M. Marin, P. Pudlo, A. Estoup and C. RobertABC and indirect inference: C. C. DrovandiHigh-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. ViolaInformed Choices: How to calibrate ABC with hypothesis testing: O. Ratmann, A. Camacho, S. Hu and C. ColjinApproximating the likelihood in approximate Bayesian computation: C. C. Drovandi, C. Grazian, K. Mengersen and C. RobertSoftware: D.WegmannDivide and conquer in ABC: Expectation-Propagation algorithms for likelihood-free inference: S. Barthelme, N. Chopin and V. CottetSMC-ABC methods for estimation of stochastic simulation models of the limit order book: G.W. Peters, E. Panayi and F. SeptierInferences on the acquisition of multidrug resistance in Mycobacterium tuberculosis using molecular epidemiological data: G. S. Rodrigues, S. A. Sisson, M. M. TanakaABC in Systems Biology: J. Liepe and M. P. H. StumpfApplication of approximate Bayesian computation to make inference about the genetic history of Pygmy hunter-gatherers populations from Western Central Africa: A. Estoup et alABC for climate: dealing with expensive simulators: P. B. Holden, N. R. Edwards, J. Hensman and R. D. WilkinsonABC in ecological modelling: M. Fasiolo and S. N. WoodABC in Nuclear Imaging: Y. Fan, S. R. Meikle, G. Angelis and A. Sitek

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

• IntroductionOverview of approximate Bayesian computation: S. A. Sisson, Y. Fan and M. A. BeaumontOn the history of ABC: S.TavareRegression approaches: M. G. B. BlumMonte Carlo samplers for ABC: Y. Fan and S. A. SissonSummary statistics: D. PrangleLikelihood-free model choose: J.-M. Marin, P. Pudlo, A. Estoup and C. RobertABC and indirect inference: C. C. DrovandiHigh-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. ViolaInformed Choices: How to calibrate ABC with hypothesis testing: O. Ratmann, A. Camacho, S. Hu and C. ColjinApproximating the likelihood in approximate Bayesian computation: C. C. Drovandi, C. Grazian, K. Mengersen and C. RobertSoftware: D.WegmannDivide and conquer in ABC: Expectation-Propagation algorithms for likelihood-free inference: S. Barthelme, N. Chopin and V. CottetSMC-ABC methods for estimation of stochastic simulation models of the limit order book: G.W. Peters, E. Panayi and F. SeptierInferences on the acquisition of multidrug resistance in Mycobacterium tuberculosis using molecular epidemiological data: G. S. Rodrigues, S. A. Sisson, M. M. TanakaABC in Systems Biology: J. Liepe and M. P. H. StumpfApplication of approximate Bayesian computation to make inference about the genetic history of Pygmy hunter-gatherers populations from Western Central Africa: A. Estoup et alABC for climate: dealing with expensive simulators: P. B. Holden, N. R. Edwards, J. Hensman and R. D. WilkinsonABC in ecological modelling: M. Fasiolo and S. N. WoodABC in Nuclear Imaging: Y. Fan, S. R. Meikle, G. Angelis and A. Sitek