Conditional independence testing, two sample comparison and density estimation using neural networks

Detalhes bibliográficos
Autor(a) principal: Inácio, Marco Henrique de Almeida
Data de Publicação: 2020
Tipo de documento: Tese
Idioma: eng
Título da fonte: Repositório Institucional da UFSCAR
Texto Completo: https://repositorio.ufscar.br/handle/ufscar/13119
Resumo: Given the vast amount of data available nowadays and the rapid increase of computational processing power, the field of machine learning and the so called algorithmic modeling have seen a recent surge in its popularity and applicability. One of the tools which has attracted great popularity is artificial neural networks due, to among other things, their versatility, ability to capture complex relations and computational scalability. In this work, we therefore apply such machine learning tools into three important problems of Statistics: two-sample comparison, conditional independence testing and conditional density estimation.
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spelling Inácio, Marco Henrique de AlmeidaIzbicki, Rafaelhttp://lattes.cnpq.br/9991192137633896http://lattes.cnpq.br/1931901020027887a65647fb-bbb0-4484-9ccd-4808b3657ab72020-08-05T12:14:20Z2020-08-05T12:14:20Z2020-08-03INÁCIO, Marco Henrique de Almeida. Conditional independence testing, two sample comparison and density estimation using neural networks. 2020. Tese (Doutorado em Estatística) – Universidade Federal de São Carlos, São Carlos, 2020. Disponível em: https://repositorio.ufscar.br/handle/ufscar/13119.https://repositorio.ufscar.br/handle/ufscar/13119Given the vast amount of data available nowadays and the rapid increase of computational processing power, the field of machine learning and the so called algorithmic modeling have seen a recent surge in its popularity and applicability. One of the tools which has attracted great popularity is artificial neural networks due, to among other things, their versatility, ability to capture complex relations and computational scalability. In this work, we therefore apply such machine learning tools into three important problems of Statistics: two-sample comparison, conditional independence testing and conditional density estimation.Dada a grande quantidade de dados disponíveis nos dias de hoje e o rápido aumento da capacidade de processamento computacional, o campo de aprendizado de máquina e a assim chamada modelagem algorítmica tem visto um grande surto de popularidade e aplicabilidade. Uma das ferramentas que atraíram grande popularidade são as redes neurais artificiais dada, entre outras coisas, sua versatilidade, habilidade de capturar relações complexas e sua escalabilidade computacional. Assim sendo, neste trabalho aplicamos estas ferramentas de aprendizado de máquina em três problemas importantes da Estatística: comparação de populações, teste de independência condicional e estimação de densidades condicionais.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)CAPES: Código de Financiamento 001engUniversidade Federal de São CarlosCâmpus São CarlosPrograma Interinstitucional de Pós-Graduação em Estatística - PIPGEsUFSCarAttribution-NonCommercial-NoDerivs 3.0 Brazilhttp://creativecommons.org/licenses/by-nc-nd/3.0/br/info:eu-repo/semantics/openAccessRedes neurais artificiaisEstimação de densidade condicionalTeste de independência condicionalComparação de populaçõesAprendizado de máquinaArtificial neural networksConditional density estimationConditional independence testingTwo-sample comparisonMachine learningCIENCIAS EXATAS E DA TERRA::PROBABILIDADE E ESTATISTICAConditional independence testing, two sample comparison and density estimation using neural networksEstimação de densidades, comparação de amostras e medidas de importância usando redes neuraisinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesis6003e57f161-19fe-4345-9e87-bc60eb7be98freponame:Repositório Institucional da UFSCARinstname:Universidade Federal de São Carlos (UFSCAR)instacron:UFSCARORIGINALthesis_ufscar.pdfthesis_ufscar.pdfapplication/pdf3602444https://repositorio.ufscar.br/bitstream/ufscar/13119/1/thesis_ufscar.pdfb4fad072cca9173cdb9072ba9fbcfc08MD51cartacomprovantepipges_para_ufscar.pdfcartacomprovantepipges_para_ufscar.pdfapplication/pdf92508https://repositorio.ufscar.br/bitstream/ufscar/13119/3/cartacomprovantepipges_para_ufscar.pdfbae476a88eb7446605ee1dc1246386d5MD53CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8811https://repositorio.ufscar.br/bitstream/ufscar/13119/4/license_rdfe39d27027a6cc9cb039ad269a5db8e34MD54TEXTthesis_ufscar.pdf.txtthesis_ufscar.pdf.txtExtracted texttext/plain177652https://repositorio.ufscar.br/bitstream/ufscar/13119/5/thesis_ufscar.pdf.txt01df421af0bd146f0d5188c9709feff7MD55cartacomprovantepipges_para_ufscar.pdf.txtcartacomprovantepipges_para_ufscar.pdf.txtExtracted texttext/plain1280https://repositorio.ufscar.br/bitstream/ufscar/13119/7/cartacomprovantepipges_para_ufscar.pdf.txt55e8736a818e23415a3e5afaf5f50284MD57THUMBNAILthesis_ufscar.pdf.jpgthesis_ufscar.pdf.jpgIM Thumbnailimage/jpeg6979https://repositorio.ufscar.br/bitstream/ufscar/13119/6/thesis_ufscar.pdf.jpgfb2e80da6ac2608f530deb03b724045aMD56cartacomprovantepipges_para_ufscar.pdf.jpgcartacomprovantepipges_para_ufscar.pdf.jpgIM Thumbnailimage/jpeg9288https://repositorio.ufscar.br/bitstream/ufscar/13119/8/cartacomprovantepipges_para_ufscar.pdf.jpg56e83e00cc2b4f2eaf53ef695bcac824MD58ufscar/131192023-09-18 18:31:59.454oai:repositorio.ufscar.br:ufscar/13119Repositório InstitucionalPUBhttps://repositorio.ufscar.br/oai/requestopendoar:43222023-09-18T18:31:59Repositório Institucional da UFSCAR - Universidade Federal de São Carlos (UFSCAR)false
dc.title.eng.fl_str_mv Conditional independence testing, two sample comparison and density estimation using neural networks
dc.title.alternative.por.fl_str_mv Estimação de densidades, comparação de amostras e medidas de importância usando redes neurais
title Conditional independence testing, two sample comparison and density estimation using neural networks
spellingShingle Conditional independence testing, two sample comparison and density estimation using neural networks
Inácio, Marco Henrique de Almeida
Redes neurais artificiais
Estimação de densidade condicional
Teste de independência condicional
Comparação de populações
Aprendizado de máquina
Artificial neural networks
Conditional density estimation
Conditional independence testing
Two-sample comparison
Machine learning
CIENCIAS EXATAS E DA TERRA::PROBABILIDADE E ESTATISTICA
title_short Conditional independence testing, two sample comparison and density estimation using neural networks
title_full Conditional independence testing, two sample comparison and density estimation using neural networks
title_fullStr Conditional independence testing, two sample comparison and density estimation using neural networks
title_full_unstemmed Conditional independence testing, two sample comparison and density estimation using neural networks
title_sort Conditional independence testing, two sample comparison and density estimation using neural networks
author Inácio, Marco Henrique de Almeida
author_facet Inácio, Marco Henrique de Almeida
author_role author
dc.contributor.authorlattes.por.fl_str_mv http://lattes.cnpq.br/1931901020027887
dc.contributor.author.fl_str_mv Inácio, Marco Henrique de Almeida
dc.contributor.advisor1.fl_str_mv Izbicki, Rafael
dc.contributor.advisor1Lattes.fl_str_mv http://lattes.cnpq.br/9991192137633896
dc.contributor.authorID.fl_str_mv a65647fb-bbb0-4484-9ccd-4808b3657ab7
contributor_str_mv Izbicki, Rafael
dc.subject.por.fl_str_mv Redes neurais artificiais
Estimação de densidade condicional
Teste de independência condicional
Comparação de populações
Aprendizado de máquina
topic Redes neurais artificiais
Estimação de densidade condicional
Teste de independência condicional
Comparação de populações
Aprendizado de máquina
Artificial neural networks
Conditional density estimation
Conditional independence testing
Two-sample comparison
Machine learning
CIENCIAS EXATAS E DA TERRA::PROBABILIDADE E ESTATISTICA
dc.subject.eng.fl_str_mv Artificial neural networks
Conditional density estimation
Conditional independence testing
Two-sample comparison
Machine learning
dc.subject.cnpq.fl_str_mv CIENCIAS EXATAS E DA TERRA::PROBABILIDADE E ESTATISTICA
description Given the vast amount of data available nowadays and the rapid increase of computational processing power, the field of machine learning and the so called algorithmic modeling have seen a recent surge in its popularity and applicability. One of the tools which has attracted great popularity is artificial neural networks due, to among other things, their versatility, ability to capture complex relations and computational scalability. In this work, we therefore apply such machine learning tools into three important problems of Statistics: two-sample comparison, conditional independence testing and conditional density estimation.
publishDate 2020
dc.date.accessioned.fl_str_mv 2020-08-05T12:14:20Z
dc.date.available.fl_str_mv 2020-08-05T12:14:20Z
dc.date.issued.fl_str_mv 2020-08-03
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/doctoralThesis
format doctoralThesis
status_str publishedVersion
dc.identifier.citation.fl_str_mv INÁCIO, Marco Henrique de Almeida. Conditional independence testing, two sample comparison and density estimation using neural networks. 2020. Tese (Doutorado em Estatística) – Universidade Federal de São Carlos, São Carlos, 2020. Disponível em: https://repositorio.ufscar.br/handle/ufscar/13119.
dc.identifier.uri.fl_str_mv https://repositorio.ufscar.br/handle/ufscar/13119
identifier_str_mv INÁCIO, Marco Henrique de Almeida. Conditional independence testing, two sample comparison and density estimation using neural networks. 2020. Tese (Doutorado em Estatística) – Universidade Federal de São Carlos, São Carlos, 2020. Disponível em: https://repositorio.ufscar.br/handle/ufscar/13119.
url https://repositorio.ufscar.br/handle/ufscar/13119
dc.language.iso.fl_str_mv eng
language eng
dc.relation.confidence.fl_str_mv 600
dc.relation.authority.fl_str_mv 3e57f161-19fe-4345-9e87-bc60eb7be98f
dc.rights.driver.fl_str_mv Attribution-NonCommercial-NoDerivs 3.0 Brazil
http://creativecommons.org/licenses/by-nc-nd/3.0/br/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Attribution-NonCommercial-NoDerivs 3.0 Brazil
http://creativecommons.org/licenses/by-nc-nd/3.0/br/
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Universidade Federal de São Carlos
Câmpus São Carlos
dc.publisher.program.fl_str_mv Programa Interinstitucional de Pós-Graduação em Estatística - PIPGEs
dc.publisher.initials.fl_str_mv UFSCar
publisher.none.fl_str_mv Universidade Federal de São Carlos
Câmpus São Carlos
dc.source.none.fl_str_mv reponame:Repositório Institucional da UFSCAR
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reponame_str Repositório Institucional da UFSCAR
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