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