Blum, M.G.B., François, O.: Non-linear regression models for approximate Bayesian computation. Stat. Comput. 20(1), 63–73 (2010) ArticleMathSciNet Google Scholar
Cabras, S., Castellanos, M.E., Ruli, E.: A Quasi likelihood approximation of posterior distributions for likelihood-intractable complex models. Metron 72(2), 153–67 (2014) ArticleMathSciNet Google Scholar
Cook, S.R., Gelman, A., Rubin, D.B.: Validation of software for Bayesian models using posterior quantiles. J. Comput. Graph. Stat. 15(3), 675–92 (2006) ArticleMathSciNet Google Scholar
Cucala, L., Marin, J.-M., Robert, C.P., Titterington, D.M.: A Bayesian reassessment of nearest-neighbor classification. J. Am. Stat. Assoc. 104(485), 73–263 (2009) ArticleMathSciNet Google Scholar
Del Moral, P., Doucet, A., Jasra, A.: An adaptive sequential Monte Carlo method for approximate Bayesian computation. Stat. Comput. 22(5), 1009–1020 (2012) ArticleMATHMathSciNet Google Scholar
Douc, R., Cappé, O., Moulines, E.: Comparison of resampling schemes for particle filtering. In: Proceedings of 4th International Symposium image and signal processing and analysis (ISPA), pp. 64–69 (2005)
Drovandi, C.C., Pettitt, A.N.: Estimation of parameters for macroparasite population evolution using approximate Bayesian computation. Biometrics 67(1), 225–33 (2011) ArticleMATHMathSciNet Google Scholar
Drovandi, C.C., Pettitt, A.N., Faddy, M.J.: Approximate Bayesian computation using indirect inference. J. R. Stat. Soc. Ser. C 60(3), 317–37 (2011) ArticleMathSciNet Google Scholar
Eddelbuettel, D., Sanderson, C.: RcppArmadillo: accelerating R with high-performance C++ linear algebra. Comput. Stat. Data Anal. 71, 1054–1063 (2014) ArticleMathSciNet Google Scholar
Everitt, R.G.: Bayesian parameter estimation for latent Markov random fields and social networks. J. Comput. Graph. Stat. 21(4), 940–60 (2012) ArticleMathSciNet Google Scholar
Filippi, S., Barnes, C.P., Cornebise, J., Stumpf, M.P.H.: On optimality of kernels for approximate Bayesian computation using sequential Monte Carlo. Stat. Appl. Genet. Mol. Biol. 12(1), 87–107 (2013) MathSciNet Google Scholar
Friel, N., Pettitt, A.N.: Classification using distance nearest neighbours. Stat. Comput. 21(3), 431–37 (2011) ArticleMathSciNet Google Scholar
Gouriéroux, C., Monfort, A., Renault, E.: Indirect inference. J. Appl. Econom. 8(S1), S85–S118 (1993)
Grelaud, A., Robert, C.P., Marin, J.M., Rodolphe, F., Taly, J.F.: ABC likelihood-free methods for model choice in Gibbs random fields. Bayesian Anal. 4(2), 317–36 (2009)
Higdon, D.M.: Auxiliary variable methods for Markov chain Monte Carlo with applications. J. Am. Stat. Assoc. 93(442), 585–95 (1998)
Hurn, M.A.: Difficulties in the use of auxiliary variables in Markov chain Monte Carlo methods. Stat. Comput. 7, 35–44 (1997) Article Google Scholar
Jasra, A., Singh, S.S., Martin, J.S., McCoy, E.: Filtering via approximate Bayesian computation. Stat. Comput. 22, 1223–37 (2012) ArticleMATHMathSciNet Google Scholar
Kitagawa, G.: Monte Carlo filter and smoother for non-Gaussian nonlinear state space models. J. Comput. Graph. Stat. 5(1), 1–25 (1996) MathSciNet Google Scholar
Liu, J.S.: Monte Carlo Strategies in Scientific Computing. Springer, New York (2001) MATH Google Scholar
McGrory, C.A., Titterington, D., Reeves, R., Pettitt, A.N.: Variational Bayes for estimating the parameters of a hidden Potts model. Stat. Comput. 19(3), 329–40 (2009) ArticleMathSciNet Google Scholar
Murray, I., Ghahramani, Z., MacKay, D.J.C.: MCMC for doubly-intractable distributions. In: Proceedings of 22nd Conference UAI, pp. 359–66, AUAI Press, Arlington, VA (2006)
Pritchard, J.K., Seielstad, M.T., Perez-Lezaun, A., Feldman, M.W.: Population growth of human Y chromosomes: a study of Y chromosome microsatellites. Mol. Biol. Evol. 16(12), 1791–1798 (1999) Article Google Scholar
Ratmann, O., Camacho, A., Meijer, A., Donker, G.: Statistical modelling of summary values leads to accurate Approximate Bayesian Computations. Techical report (2014). arXiv:1305.4283
Sedki, M., Pudlo, P., Marin, J.-M., Robert, C.P., Cornuet, J.-M.: Efficient learning in ABC algorithms. Technical report (2013). arXiv:1210.1388
Sisson, S.A., Fan, Y., Tanaka, M.M.: Sequential Monte Carlo without likelihoods. Proc. Natl. Acad. Sci. 104(6), 1760–1765 (2007) ArticleMATHMathSciNet Google Scholar
Stoehr, J., Pudlo, P., Cucala, L.: Geometric summary statistics for ABC model choice between hidden Gibbs random fields. Stat. Comput. (2014). doi:10.1007/s11222-014-9514-9
Swendsen, R.H., Wang, J.S.: Nonuniversal critical dynamics in Monte Carlo simulations. Phys. Rev. Lett. 58, 86–88 (1987) Article Google Scholar
Toni, T., Welch, D., Strelkowa, N., Ipsen, A., Stumpf, M.P.H.: Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems. J. R. Soc. Interface 6(31), 187–202 (2009) Article Google Scholar
Wilkinson, R.: accelerating ABC methods using Gaussian processes. In: Proceedings of 17th International Conference AISTATS, JMLR W&CP 33, 1015–23 (2014)
Winkler, G.: Image Analysis, Random Fields and Markov chain Monte Carlo Methods: A Mathematical Introduction, 2nd edn. Springer-Verlag, Heidelberg (2003) Book Google Scholar
Wood, S.N.: Statistical inference for noisy nonlinear ecological dynamic systems. Nature 466, 1102–1104 (2010) Article Google Scholar