Likelihood-free parallel tempering
Abstract Approximate Bayesian Computational (ABC) methods, or likelihood-free methods, have appeared in the past fifteen years as useful methods to perform Bayesian analysis when the likelihood is analytically or computationally intractable. Several ABC methods have been proposed: MCMC methods have...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Baragatti, Meïli [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2012 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media, LLC 2012 |
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| Übergeordnetes Werk: |
Enthalten in: Statistics and computing - Springer US, 1991, 23(2012), 4 vom: 07. Apr., Seite 535-549 |
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| Übergeordnetes Werk: |
volume:23 ; year:2012 ; number:4 ; day:07 ; month:04 ; pages:535-549 |
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| DOI / URN: |
10.1007/s11222-012-9328-6 |
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OLC2033745619 |
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| 520 | |a Abstract Approximate Bayesian Computational (ABC) methods, or likelihood-free methods, have appeared in the past fifteen years as useful methods to perform Bayesian analysis when the likelihood is analytically or computationally intractable. Several ABC methods have been proposed: MCMC methods have been developed by Marjoram et al. (2003) and by Bortot et al. (2007) for instance, and sequential methods have been proposed among others by Sisson et al. (2007), Beaumont et al. (2009) and Del Moral et al. (2012). Recently, sequential ABC methods have appeared as an alternative to ABC-PMC methods (see for instance McKinley et al., 2009; Sisson et al., 2007). In this paper a new algorithm combining population-based MCMC methods with ABC requirements is proposed, using an analogy with the parallel tempering algorithm (Geyer 1991). Performance is compared with existing ABC algorithms on simulations and on a real example. | ||
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| 700 | 1 | |a Pommeret, Denys |4 aut | |
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10.1007/s11222-012-9328-6 doi (DE-627)OLC2033745619 (DE-He213)s11222-012-9328-6-p DE-627 ger DE-627 rakwb eng 004 620 VZ Baragatti, Meïli verfasserin aut Likelihood-free parallel tempering 2012 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2012 Abstract Approximate Bayesian Computational (ABC) methods, or likelihood-free methods, have appeared in the past fifteen years as useful methods to perform Bayesian analysis when the likelihood is analytically or computationally intractable. Several ABC methods have been proposed: MCMC methods have been developed by Marjoram et al. (2003) and by Bortot et al. (2007) for instance, and sequential methods have been proposed among others by Sisson et al. (2007), Beaumont et al. (2009) and Del Moral et al. (2012). Recently, sequential ABC methods have appeared as an alternative to ABC-PMC methods (see for instance McKinley et al., 2009; Sisson et al., 2007). In this paper a new algorithm combining population-based MCMC methods with ABC requirements is proposed, using an analogy with the parallel tempering algorithm (Geyer 1991). Performance is compared with existing ABC algorithms on simulations and on a real example. Approximated Bayesian computational Likelihood-free Intractable likelihood Parallel tempering Population-based Monte Carlo Markov chain Grimaud, Agnès aut Pommeret, Denys aut Enthalten in Statistics and computing Springer US, 1991 23(2012), 4 vom: 07. Apr., Seite 535-549 (DE-627)131007963 (DE-600)1087487-2 (DE-576)052732894 0960-3174 nnns volume:23 year:2012 number:4 day:07 month:04 pages:535-549 https://doi.org/10.1007/s11222-012-9328-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2012 GBV_ILN_4126 AR 23 2012 4 07 04 535-549 |
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10.1007/s11222-012-9328-6 doi (DE-627)OLC2033745619 (DE-He213)s11222-012-9328-6-p DE-627 ger DE-627 rakwb eng 004 620 VZ Baragatti, Meïli verfasserin aut Likelihood-free parallel tempering 2012 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2012 Abstract Approximate Bayesian Computational (ABC) methods, or likelihood-free methods, have appeared in the past fifteen years as useful methods to perform Bayesian analysis when the likelihood is analytically or computationally intractable. Several ABC methods have been proposed: MCMC methods have been developed by Marjoram et al. (2003) and by Bortot et al. (2007) for instance, and sequential methods have been proposed among others by Sisson et al. (2007), Beaumont et al. (2009) and Del Moral et al. (2012). Recently, sequential ABC methods have appeared as an alternative to ABC-PMC methods (see for instance McKinley et al., 2009; Sisson et al., 2007). In this paper a new algorithm combining population-based MCMC methods with ABC requirements is proposed, using an analogy with the parallel tempering algorithm (Geyer 1991). Performance is compared with existing ABC algorithms on simulations and on a real example. Approximated Bayesian computational Likelihood-free Intractable likelihood Parallel tempering Population-based Monte Carlo Markov chain Grimaud, Agnès aut Pommeret, Denys aut Enthalten in Statistics and computing Springer US, 1991 23(2012), 4 vom: 07. Apr., Seite 535-549 (DE-627)131007963 (DE-600)1087487-2 (DE-576)052732894 0960-3174 nnns volume:23 year:2012 number:4 day:07 month:04 pages:535-549 https://doi.org/10.1007/s11222-012-9328-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2012 GBV_ILN_4126 AR 23 2012 4 07 04 535-549 |
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10.1007/s11222-012-9328-6 doi (DE-627)OLC2033745619 (DE-He213)s11222-012-9328-6-p DE-627 ger DE-627 rakwb eng 004 620 VZ Baragatti, Meïli verfasserin aut Likelihood-free parallel tempering 2012 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2012 Abstract Approximate Bayesian Computational (ABC) methods, or likelihood-free methods, have appeared in the past fifteen years as useful methods to perform Bayesian analysis when the likelihood is analytically or computationally intractable. Several ABC methods have been proposed: MCMC methods have been developed by Marjoram et al. (2003) and by Bortot et al. (2007) for instance, and sequential methods have been proposed among others by Sisson et al. (2007), Beaumont et al. (2009) and Del Moral et al. (2012). Recently, sequential ABC methods have appeared as an alternative to ABC-PMC methods (see for instance McKinley et al., 2009; Sisson et al., 2007). In this paper a new algorithm combining population-based MCMC methods with ABC requirements is proposed, using an analogy with the parallel tempering algorithm (Geyer 1991). Performance is compared with existing ABC algorithms on simulations and on a real example. Approximated Bayesian computational Likelihood-free Intractable likelihood Parallel tempering Population-based Monte Carlo Markov chain Grimaud, Agnès aut Pommeret, Denys aut Enthalten in Statistics and computing Springer US, 1991 23(2012), 4 vom: 07. Apr., Seite 535-549 (DE-627)131007963 (DE-600)1087487-2 (DE-576)052732894 0960-3174 nnns volume:23 year:2012 number:4 day:07 month:04 pages:535-549 https://doi.org/10.1007/s11222-012-9328-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2012 GBV_ILN_4126 AR 23 2012 4 07 04 535-549 |
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10.1007/s11222-012-9328-6 doi (DE-627)OLC2033745619 (DE-He213)s11222-012-9328-6-p DE-627 ger DE-627 rakwb eng 004 620 VZ Baragatti, Meïli verfasserin aut Likelihood-free parallel tempering 2012 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2012 Abstract Approximate Bayesian Computational (ABC) methods, or likelihood-free methods, have appeared in the past fifteen years as useful methods to perform Bayesian analysis when the likelihood is analytically or computationally intractable. Several ABC methods have been proposed: MCMC methods have been developed by Marjoram et al. (2003) and by Bortot et al. (2007) for instance, and sequential methods have been proposed among others by Sisson et al. (2007), Beaumont et al. (2009) and Del Moral et al. (2012). Recently, sequential ABC methods have appeared as an alternative to ABC-PMC methods (see for instance McKinley et al., 2009; Sisson et al., 2007). In this paper a new algorithm combining population-based MCMC methods with ABC requirements is proposed, using an analogy with the parallel tempering algorithm (Geyer 1991). Performance is compared with existing ABC algorithms on simulations and on a real example. Approximated Bayesian computational Likelihood-free Intractable likelihood Parallel tempering Population-based Monte Carlo Markov chain Grimaud, Agnès aut Pommeret, Denys aut Enthalten in Statistics and computing Springer US, 1991 23(2012), 4 vom: 07. Apr., Seite 535-549 (DE-627)131007963 (DE-600)1087487-2 (DE-576)052732894 0960-3174 nnns volume:23 year:2012 number:4 day:07 month:04 pages:535-549 https://doi.org/10.1007/s11222-012-9328-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2012 GBV_ILN_4126 AR 23 2012 4 07 04 535-549 |
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Abstract Approximate Bayesian Computational (ABC) methods, or likelihood-free methods, have appeared in the past fifteen years as useful methods to perform Bayesian analysis when the likelihood is analytically or computationally intractable. Several ABC methods have been proposed: MCMC methods have been developed by Marjoram et al. (2003) and by Bortot et al. (2007) for instance, and sequential methods have been proposed among others by Sisson et al. (2007), Beaumont et al. (2009) and Del Moral et al. (2012). Recently, sequential ABC methods have appeared as an alternative to ABC-PMC methods (see for instance McKinley et al., 2009; Sisson et al., 2007). In this paper a new algorithm combining population-based MCMC methods with ABC requirements is proposed, using an analogy with the parallel tempering algorithm (Geyer 1991). Performance is compared with existing ABC algorithms on simulations and on a real example. © Springer Science+Business Media, LLC 2012 |
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Abstract Approximate Bayesian Computational (ABC) methods, or likelihood-free methods, have appeared in the past fifteen years as useful methods to perform Bayesian analysis when the likelihood is analytically or computationally intractable. Several ABC methods have been proposed: MCMC methods have been developed by Marjoram et al. (2003) and by Bortot et al. (2007) for instance, and sequential methods have been proposed among others by Sisson et al. (2007), Beaumont et al. (2009) and Del Moral et al. (2012). Recently, sequential ABC methods have appeared as an alternative to ABC-PMC methods (see for instance McKinley et al., 2009; Sisson et al., 2007). In this paper a new algorithm combining population-based MCMC methods with ABC requirements is proposed, using an analogy with the parallel tempering algorithm (Geyer 1991). Performance is compared with existing ABC algorithms on simulations and on a real example. © Springer Science+Business Media, LLC 2012 |
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Abstract Approximate Bayesian Computational (ABC) methods, or likelihood-free methods, have appeared in the past fifteen years as useful methods to perform Bayesian analysis when the likelihood is analytically or computationally intractable. Several ABC methods have been proposed: MCMC methods have been developed by Marjoram et al. (2003) and by Bortot et al. (2007) for instance, and sequential methods have been proposed among others by Sisson et al. (2007), Beaumont et al. (2009) and Del Moral et al. (2012). Recently, sequential ABC methods have appeared as an alternative to ABC-PMC methods (see for instance McKinley et al., 2009; Sisson et al., 2007). In this paper a new algorithm combining population-based MCMC methods with ABC requirements is proposed, using an analogy with the parallel tempering algorithm (Geyer 1991). Performance is compared with existing ABC algorithms on simulations and on a real example. © Springer Science+Business Media, LLC 2012 |
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Likelihood-free parallel tempering |
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Grimaud, Agnès Pommeret, Denys |
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10.1007/s11222-012-9328-6 |
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