Zobrazeno 1 - 10
of 28
pro vyhledávání: '"Reversible jump Markov chain Monte Carlo methods"'
Publikováno v:
SoftwareX, Vol 14, Iss , Pp 100664- (2021)
Reversible jump Markov chain Monte Carlo (RJMCMC) is a powerful Bayesian trans-dimensional algorithm for performing model selection while inferring the distribution of model parameters. The present work introduces CU-MSDSp as an open source and fully
Externí odkaz:
https://doaj.org/article/1f901cf205a34cf6973aa81f300aeb67
Autor:
Newton, Michael A., Hastie, David I.
Publikováno v:
Journal of the Royal Statistical Society. Series C (Applied Statistics), 2006 Jan 01. 55(1), 123-138.
Externí odkaz:
https://www.jstor.org/stable/3592592
Autor:
Knorr-Held, Leonhard, Best, Nicola G.
Publikováno v:
Journal of the Royal Statistical Society. Series A (Statistics in Society), 2001 Jan 01. 164(1), 73-85.
Externí odkaz:
https://www.jstor.org/stable/2680535
Autor:
Pievatolo, Antonio, Rotondi, Renata
Publikováno v:
Journal of the Royal Statistical Society. Series C (Applied Statistics), 2000 Jan 01. 49(4), 543-562.
Externí odkaz:
https://www.jstor.org/stable/2680787
Publikováno v:
Journal of the Royal Statistical Society. Series C (Applied Statistics), 2009 Jul 01. 58(3), 383-403.
Externí odkaz:
https://www.jstor.org/stable/25578171
Publikováno v:
SoftwareX, Vol 14, Iss, Pp 100664-(2021)
Reversible jump Markov chain Monte Carlo (RJMCMC) is a powerful Bayesian trans-dimensional algorithm for performing model selection while inferring the distribution of model parameters. The present work introduces CU-MSDSp as an open source and fully
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d22c723b50d5a6a0126b1c5533c6b2ff
http://www.sciencedirect.com/science/article/pii/S2352711021000091
http://www.sciencedirect.com/science/article/pii/S2352711021000091
Akademický článek
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Autor:
Renata Rotondi, Antonio Pievatolo
Publikováno v:
Applied statistics
49 (2000): 543–562. doi:10.1111/1467-9876.00211
info:cnr-pdr/source/autori:A. Pievatolo and R. Rotondi/titolo:Analysing the interevent time distribution to identify seismicity phases: a Bayesian nonparametric approach to the multiple-changepoint problem/doi:10.1111%2F1467-9876.00211/rivista:Applied statistics (Print)/anno:2000/pagina_da:543/pagina_a:562/intervallo_pagine:543–562/volume:49
49 (2000): 543–562. doi:10.1111/1467-9876.00211
info:cnr-pdr/source/autori:A. Pievatolo and R. Rotondi/titolo:Analysing the interevent time distribution to identify seismicity phases: a Bayesian nonparametric approach to the multiple-changepoint problem/doi:10.1111%2F1467-9876.00211/rivista:Applied statistics (Print)/anno:2000/pagina_da:543/pagina_a:562/intervallo_pagine:543–562/volume:49
SUMMARY In the study of earthquakes, several aspects of the underlying physical process, such as the time non-stationarity of the process, are not yet well understood, because we lack clear indications about its evolution in time. Taking as our point
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4aa32f8d6fc876964fd4581c6dc4d9a9
https://doi.org/10.1111/1467-9876.00211
https://doi.org/10.1111/1467-9876.00211
Autor:
Mingfei Li, Sarat C. Dass
Publikováno v:
Ann. Appl. Stat. 3, no. 4 (2009), 1448-1466
The study of fingerprint individuality aims to determine to what extent a fingerprint uniquely identifies an individual. Recent court cases have highlighted the need for measures of fingerprint individuality when a person is identified based on finge
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a15a37442ada9eaf676acc982ca2cd8a
Akademický článek
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