Markov-chain approach to the distribution of ancestors in species of biparental reproduction

Autores
Caruso, M.; Jarne, Cecilia Gisele
Año de publicación
2014
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
We studied how to obtain a distribution for the number of ancestors in species of sexual reproduction. Present models concentrate on the estimation of distributions repetitions of ancestors in genealogical trees. It has been shown that it is not possible to reconstruct the genealogical history of each species along all its generations by means of a geometric progression. This analysis demonstrates that it is possible to rebuild the tree of progenitors by modeling the problem with a Markov chain. For each generation, the maximum number of possible ancestors is different. This presents huge problems for the resolution. We found a solution through a dilation of the sample space, although the distribution defined there takes smaller values with respect to the initial problem. In order to correct the distribution for each generation, we introduced the invariance under a gauge (local) group of dilations. These ideas can be used to study the interaction of several processes and provide a new approach on the problem of the common ancestor. In the same direction, this model also provides some elements that can be used to improve models of animal reproduction.
Instituto de Física La Plata
Materia
Física
Sample space
Geometric progression
Dilation (metric space)
Distribution (number theory)
Mathematics
Theoretical computer science
Stochastic process
Tree (graph theory)
Group (mathematics)
Markov chain
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/125913
Acceder
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oai_identifier_str oai:sedici.unlp.edu.ar:10915/125913
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Markov-chain approach to the distribution of ancestors in species of biparental reproductionCaruso, M.Jarne, Cecilia GiseleFísicaSample spaceGeometric progressionDilation (metric space)Distribution (number theory)MathematicsTheoretical computer scienceStochastic processTree (graph theory)Group (mathematics)Markov chainWe studied how to obtain a distribution for the number of ancestors in species of sexual reproduction. Present models concentrate on the estimation of distributions repetitions of ancestors in genealogical trees. It has been shown that it is not possible to reconstruct the genealogical history of each species along all its generations by means of a geometric progression. This analysis demonstrates that it is possible to rebuild the tree of progenitors by modeling the problem with a Markov chain. For each generation, the maximum number of possible ancestors is different. This presents huge problems for the resolution. We found a solution through a dilation of the sample space, although the distribution defined there takes smaller values with respect to the initial problem. In order to correct the distribution for each generation, we introduced the invariance under a gauge (local) group of dilations. These ideas can be used to study the interaction of several processes and provide a new approach on the problem of the common ancestor. In the same direction, this model also provides some elements that can be used to improve models of animal reproduction.Instituto de Física La Plata2014info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/125913enginfo:eu-repo/semantics/altIdentifier/issn/1550-2376info:eu-repo/semantics/altIdentifier/issn/1539-3755info:eu-repo/semantics/altIdentifier/arxiv/1109.2672info:eu-repo/semantics/altIdentifier/pmid/25215707info:eu-repo/semantics/altIdentifier/doi/10.1103/physreve.90.022125info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-03T11:02:19Zoai:sedici.unlp.edu.ar:10915/125913Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-03 11:02:19.8SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Markov-chain approach to the distribution of ancestors in species of biparental reproduction
title Markov-chain approach to the distribution of ancestors in species of biparental reproduction
spellingShingle Markov-chain approach to the distribution of ancestors in species of biparental reproduction
Caruso, M.
Física
Sample space
Geometric progression
Dilation (metric space)
Distribution (number theory)
Mathematics
Theoretical computer science
Stochastic process
Tree (graph theory)
Group (mathematics)
Markov chain
title_short Markov-chain approach to the distribution of ancestors in species of biparental reproduction
title_full Markov-chain approach to the distribution of ancestors in species of biparental reproduction
title_fullStr Markov-chain approach to the distribution of ancestors in species of biparental reproduction
title_full_unstemmed Markov-chain approach to the distribution of ancestors in species of biparental reproduction
title_sort Markov-chain approach to the distribution of ancestors in species of biparental reproduction
dc.creator.none.fl_str_mv Caruso, M.
Jarne, Cecilia Gisele
author Caruso, M.
author_facet Caruso, M.
Jarne, Cecilia Gisele
author_role author
author2 Jarne, Cecilia Gisele
author2_role author
dc.subject.none.fl_str_mv Física
Sample space
Geometric progression
Dilation (metric space)
Distribution (number theory)
Mathematics
Theoretical computer science
Stochastic process
Tree (graph theory)
Group (mathematics)
Markov chain
topic Física
Sample space
Geometric progression
Dilation (metric space)
Distribution (number theory)
Mathematics
Theoretical computer science
Stochastic process
Tree (graph theory)
Group (mathematics)
Markov chain
dc.description.none.fl_txt_mv We studied how to obtain a distribution for the number of ancestors in species of sexual reproduction. Present models concentrate on the estimation of distributions repetitions of ancestors in genealogical trees. It has been shown that it is not possible to reconstruct the genealogical history of each species along all its generations by means of a geometric progression. This analysis demonstrates that it is possible to rebuild the tree of progenitors by modeling the problem with a Markov chain. For each generation, the maximum number of possible ancestors is different. This presents huge problems for the resolution. We found a solution through a dilation of the sample space, although the distribution defined there takes smaller values with respect to the initial problem. In order to correct the distribution for each generation, we introduced the invariance under a gauge (local) group of dilations. These ideas can be used to study the interaction of several processes and provide a new approach on the problem of the common ancestor. In the same direction, this model also provides some elements that can be used to improve models of animal reproduction.
Instituto de Física La Plata
description We studied how to obtain a distribution for the number of ancestors in species of sexual reproduction. Present models concentrate on the estimation of distributions repetitions of ancestors in genealogical trees. It has been shown that it is not possible to reconstruct the genealogical history of each species along all its generations by means of a geometric progression. This analysis demonstrates that it is possible to rebuild the tree of progenitors by modeling the problem with a Markov chain. For each generation, the maximum number of possible ancestors is different. This presents huge problems for the resolution. We found a solution through a dilation of the sample space, although the distribution defined there takes smaller values with respect to the initial problem. In order to correct the distribution for each generation, we introduced the invariance under a gauge (local) group of dilations. These ideas can be used to study the interaction of several processes and provide a new approach on the problem of the common ancestor. In the same direction, this model also provides some elements that can be used to improve models of animal reproduction.
publishDate 2014
dc.date.none.fl_str_mv 2014
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Articulo
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/125913
url http://sedici.unlp.edu.ar/handle/10915/125913
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/issn/1550-2376
info:eu-repo/semantics/altIdentifier/issn/1539-3755
info:eu-repo/semantics/altIdentifier/arxiv/1109.2672
info:eu-repo/semantics/altIdentifier/pmid/25215707
info:eu-repo/semantics/altIdentifier/doi/10.1103/physreve.90.022125
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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score 13.13397

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