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While ABC and related likelihood-free methods have overwhelmingly been 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 = 2010 |title = Simulation-based model selection for dynamical systems in systems and population biology | journal = Bioinformatics |volume = 26|pages = 104 |doi = 10.1093/bioinformatics/btp619 |url=http://bioinformatics.oxfordjournals.org/cgi/reprint/26/1/104.pdf|format=PDF |pmid = 19880371 |issue = 1 |pmc = 2796821 }}</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|issue = 23|pmc = 2639274}}</ref> <ref name=Liepe>{{cite journal|author = Liepe, J.; Barnes, C.; Cule, E.; Erguler, K.; Kirk, P.; Toni, T.; Stumpf, M.P.H.|year=2010|title=ABC-SysBio—approximate Bayesian computation in Python with GPU support|journal=Bioinformatics|volume=26|pages=1797|doi=10.1093/bioinformatics/btq278|url=
Recent advances in ABC methodology, computational implementations and applications are discussed at the '''ABC in ...''' meetings:
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