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The generic procedure outlined above can be computationally inefficient but ABC and likelihood-free inferential procedures can be combined with the standard computational approaches used in [[Bayesian inference]] such as [[Markov chain Monte Carlo]] <ref name=Marjoram>{{cite journal|last = Marjoram|first = P.|coauthors = Molitor, J., Plagnol, V. and Tavaré, S.|title = Markov chain Monte Carlo without likelihoods|journal = P Natl Acad Sci USA|volume = 100|number = 26|date = 2003|pages = 15324–15328|doi = 10.1073/pnas.0306899100|pmid = 14663152}} </ref> <ref name=Plagnol>{{cite journal|last = Plagnol|first = V.|coauthors = Tavaré, S.|title = Approximate Bayesian computation and MCMC|journal = Monte Carlo and Quasi-Monte Carlo Methods 2002|year = 2004|url = http://www-gene.cimr.cam.ac.uk/vplagnol/papers/vpst-web.pdf}} (The link is to a preprint.)</ref> and [[Sequential Monte Carlo method]] <ref name=Toni2009> </ref>approaches. In these frameworks ABC can be used to tackle otherwise computationally intractable problems.
While ABC and related likelihood-free methods have overwhelmingly be employed for parameter estimation, they can also be used for [[model selection]], as the whole apparatus of Bayesian model selection can be adapted to the ABC framework <ref name= Toni2009b>{{cite journal |author = Toni, T.; Stumpf, M.P.H. |year = 2009 |title = Simulation-based model selection for dynamical systems in systems and population biology | journal = Bioinformatics |volume = (in press) |doi = doi:10.1093/bioinformatics/btp619 |url=http://bioinformatics.oxfordjournals.org/cgi/content/abstract/btp619}}</ref>.
An increasing number of software implementations of ABC approaches exist <ref name=Cornuet>{{cite journal|last = Cornuet|first = J-M.|coauthors = Santos, F., Beaumont, M. A., Robert, C. P., Marin, J-M., [[David Balding|Balding, D. J.]], Guillemaud, T. and Estoup, A.|title = Inferring population history with DIY ABC: a user-friendly approach to Approximate Bayesian Computation|journal = Bioinformatics|year = 2008|url = http://bioinformatics.oxfordjournals.org/cgi/content/abstract/btn514|pmid = 18842597|doi = 10.1093/bioinformatics/btn514|volume = 24|pages = 2713}} </ref>.
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