Conditional independence testing, two sample comparison and density estimation using neural networks
Autor(a) principal: | |
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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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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 |
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eng |
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600 |
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Attribution-NonCommercial-NoDerivs 3.0 Brazil http://creativecommons.org/licenses/by-nc-nd/3.0/br/ info:eu-repo/semantics/openAccess |
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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 |
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Programa Interinstitucional de Pós-Graduação em Estatística - PIPGEs |
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UFSCar |
publisher.none.fl_str_mv |
Universidade Federal de São Carlos Câmpus São Carlos |
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