References
- Liu JS. Monte Carlo strategies in scientific computing. New York: Springer; 2001.
- Gamerman D, Lopes HS. Markov chain Monte Carlo: stochastic simulation for Bayesian inference. 2nd ed. Boca Raton, FL: Chapman & Hall; 2006.
- Beaumont MA, Zhang W, Balding DJ. Approximate Bayesian computation in population genetics. Genetics. 2002;162:2025–2035.
- Sisson SA, Fan Y. Likelihood-free Markov chain Monte Carlo. In Brooks SP, Gelman A, Jones G, Meng XL. editors. Handbook of Markov Chain Monte Carlo. Boca Raton, FL: Chapman & Hall; 2011. p. 313–335.
- Marin J-M, Pudlo P, Robert CP, Ryder R. Approximate Bayesian computational methods. Stat Comput. 2012;22:1167–1180. doi: 10.1007/s11222-011-9288-2
- Tavaré S, Balding DJ, Griffiths RC, Donnelly P. Inferring coalescence times from DNA sequence data. Genetics. 1997;145:505–518.
- Pritchard JK, Seielstad MT, Perez-Lezaun A, Feldman MW. Population growth of human Y chromosomes: a study of Y chromosome microsatellites. Mol Biol Evol. 1999;16:1791–1798. doi: 10.1093/oxfordjournals.molbev.a026091
- Marjoram P, Molitor J, Plagnol V, Taveré S. Markov chain Monte Carlo without likelihoods. Proc Nat Acad Sci. 2003;100:15324–15328. doi: 10.1073/pnas.0306899100
- Bortot P, Coles SG, Sisson SA. Inference for stereological extremes. J Am Stat Assoc. 2007;102:84–92. doi: 10.1198/016214506000000988
- Ratmann O, Andrieu C, Wiuf C, Richardson S. Model criticism based on likelihood-free inference, with an application to protein network evolution. Proc Nat Acad Sci. 2009;106:10576–10581.
- Sisson SA, Fan Y, Tanaka MM. Sequential Monte Carlo without likelihoods. Proc Nat Acad Sci. 2007;104:1760–1765. doi: 10.1073/pnas.0607208104
- Toni T, Welch D, Strelkowa N, Ipsen A, Stumpf PH. Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems. J R Soc Interface. 2009;6:187–202. doi: 10.1098/rsif.2008.0172
- Beaumont MA, Cornuet J-M, Marin J-M, Robert CP. Adaptive approximate Bayesian computation. Biometrika. 2009;96:983–990. doi: 10.1093/biomet/asp052
- Del Moral P, Doucet A, Jasra A. An adaptive sequential Monte Carlo method for approximate Bayesian computation. Stat Comput. 2012;22:1009–1020. doi: 10.1007/s11222-011-9271-y
- Drovandi CC, Pettitt AN. Estimation of parameters for macroparasite population evolution using approximate Bayesian computation. Biometrics. 2011;67:225–233. doi: 10.1111/j.1541-0420.2010.01410.x
- Wegmann D. Leuenberger C, Excoffier L. Efficient approximate Bayesian computation coupled with Markov chain Monte Carlo without likelihood. Genetics. 2009;182:1207–1218. doi: 10.1534/genetics.109.102509
- Qin ZS, Liu JS. Multipoint Metropolis method with application to hybrid Monte Carlo. J Comput Phys. 2001;172:827–840. doi: 10.1006/jcph.2001.6860
- Liu JS, Liang F, Wong WH. The use of multiple-try method and local optimization in Metropolis sampling. J Am Stat Assoc. 2000;94:121–134. doi: 10.1080/01621459.2000.10473908
- Pandolfi S, Bartolucci F, Friel N. A generalization of the multiple-try Metropolis algorithm for Bayesian estimation and model selection. Proceedings of the 13th International Conference on Artificial Intelligence and Statistics; 2010. p. 581–588; Sardinia, Italy.
- Peters GW, Sisson SA, Fan Y. Likelihood-free Bayesian inference for alpha-stable models. Comput Stat Data Anal. 2012;56:3743–3756. doi: 10.1016/j.csda.2010.10.004
- Allingham D, King RAR, Mengersen KL. Bayesian estimation of quantile distributions. Stat Comput. 2009;19:189–201. doi: 10.1007/s11222-008-9083-x
- Murray I. Advances in Markov chain Monte Carlo methods [Ph.D. thesis]. London: Gatsby Computational Neuroscience Unit, University College London; 2007.
- Martino L, Del Olmo VP, Read J. A multi-point Metropolis scheme with generic weight functions. Stat Probabil Lett. 2012;82:1445–1453. doi: 10.1016/j.spl.2012.04.008
- Casarin R, Craiu RV, Leisen F. Interacting multiple-try algorithms with different proposal distributions. Stat Comput. 2013;23:185–200. doi: 10.1007/s11222-011-9301-9
- Chib S, Markov chain Monte Carlo methods: computation and inference. In: Heckman JJ, Leamer E, editors. Handbook of econometrics. Volume 5, Chapter 57. Amsterdam: Elsevier; 2001. p. 3569–3649.
- Baragatti M, Grimaud A, Pommeret D. Likelihood-free parallel tempering. Stat Comput. 2012; doi:10.1007/s11222-012-9328-6.
- Freimer M, Mudholkar GS, Kollia G, Lin CT. A study of the generalized Tukey lambda family. Commun Stat. 1998;17:3547–3567.
- Gilchrist W. Statistical modelling with quantile functions. Boca Raton, FL: Chapman & Hall; 2000.
- Karian ZA, Dudewicz EJ. Fitting statistical distributions: the generalized lambda distribution and generalized bootstrap methods. Boca Raton, FL: Chapman & Hall; 2000.
- Su S. GLDEX: fitting single and mixture of generalized lambda distribution (RS and FMKL) using various methods. 2010. R package version 1.0.4.1 [cited 2012 Oct 24]. Available from: http://cran.r-project.org/web/packages/GLDEX.
- Doornik JA. Object-oriented matrix programming using ox. 3rd ed. London: Timberlake Consultants Press and Oxford; 2007.