Constraint-based learning for non-parametric continuous bayesian networks View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2021-06-26

AUTHORS

Marvin Lasserre, Régis Lebrun, Pierre-Henri Wuillemin

ABSTRACT

Modeling high-dimensional multivariate distributions is a computationally challenging task. In the discrete case, Bayesian networks have been successfully used to reduce the complexity and to simplify the problem. However, they lack of a general model for continuous variables. In order to overcome this problem, Elidan (2010) proposed the model of copula Bayesian networks that parametrizes Bayesian networks using copula functions. We propose a new learning algorithm for this model based on a PC algorithm and a conditional independence test proposed by Bouezmarni et al. (2009). This test being non-parametric, no model assumptions are made allowing it to be as general as possible. This algorithm is compared on generated data with the parametric method proposed by Elidan (2010) and proves to have better results. More... »

PAGES

1035-1052

References to SciGraph publications

  • 2002. Random Generation of Bayesian Networks in ADVANCES IN ARTIFICIAL INTELLIGENCE
  • 1998. Tabu Search in HANDBOOK OF COMBINATORIAL OPTIMIZATION
  • 2010-05-25. Pair-Copula Constructions of Multivariate Copulas in COPULA THEORY AND ITS APPLICATIONS
  • 2015. OpenTURNS: An Industrial Software for Uncertainty Quantification in Simulation in HANDBOOK OF UNCERTAINTY QUANTIFICATION
  • 2003. Kendall’s Tau for Elliptical Distributions in CREDIT RISK
  • 1989. The ALARM Monitoring System: A Case Study with two Probabilistic Inference Techniques for Belief Networks in AIME 89
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s10472-021-09754-2

    DOI

    http://dx.doi.org/10.1007/s10472-021-09754-2

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1139150590


    Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
    Incoming Citations Browse incoming citations for this publication using opencitations.net

    JSON-LD is the canonical representation for SciGraph data.

    TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

    [
      {
        "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
        "about": [
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Information and Computing Sciences", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0801", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Artificial Intelligence and Image Processing", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "Sorbonne Universit\u00e9- LIP6, 4 place Jussieu, 75005, Paris, France", 
              "id": "http://www.grid.ac/institutes/grid.462844.8", 
              "name": [
                "Sorbonne Universit\u00e9- LIP6, 4 place Jussieu, 75005, Paris, France"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Lasserre", 
            "givenName": "Marvin", 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Airbus AI Research, 22 rue du Gouverneur G\u00e9n\u00e9ral Ebou\u00e9, 92130, Issy les Moulineaux, France", 
              "id": "http://www.grid.ac/institutes/None", 
              "name": [
                "Airbus AI Research, 22 rue du Gouverneur G\u00e9n\u00e9ral Ebou\u00e9, 92130, Issy les Moulineaux, France"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Lebrun", 
            "givenName": "R\u00e9gis", 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Sorbonne Universit\u00e9- LIP6, 4 place Jussieu, 75005, Paris, France", 
              "id": "http://www.grid.ac/institutes/grid.462844.8", 
              "name": [
                "Sorbonne Universit\u00e9- LIP6, 4 place Jussieu, 75005, Paris, France"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Wuillemin", 
            "givenName": "Pierre-Henri", 
            "id": "sg:person.010323340640.84", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010323340640.84"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "sg:pub.10.1007/978-3-642-12465-5_4", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1031567327", 
              "https://doi.org/10.1007/978-3-642-12465-5_4"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/3-540-36127-8_35", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1023803717", 
              "https://doi.org/10.1007/3-540-36127-8_35"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-319-11259-6_64-1", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1084933692", 
              "https://doi.org/10.1007/978-3-319-11259-6_64-1"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-642-59365-9_8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1023644261", 
              "https://doi.org/10.1007/978-3-642-59365-9_8"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-642-93437-7_28", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1012429812", 
              "https://doi.org/10.1007/978-3-642-93437-7_28"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-1-4613-0303-9_33", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1008061296", 
              "https://doi.org/10.1007/978-1-4613-0303-9_33"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2021-06-26", 
        "datePublishedReg": "2021-06-26", 
        "description": "Modeling high-dimensional multivariate distributions is a computationally challenging task. In the discrete case, Bayesian networks have been successfully used to reduce the complexity and to simplify the problem. However, they lack of a general model for continuous variables. In order to overcome this problem, Elidan (2010) proposed the model of copula Bayesian networks that parametrizes Bayesian networks using copula functions. We propose a new learning algorithm for this model based on a PC algorithm and a conditional independence test proposed by Bouezmarni et al. (2009). This test being non-parametric, no model assumptions are made allowing it to be as general as possible. This algorithm is compared on generated data with the parametric method proposed by Elidan (2010) and proves to have better results.", 
        "genre": "article", 
        "id": "sg:pub.10.1007/s10472-021-09754-2", 
        "inLanguage": "en", 
        "isAccessibleForFree": true, 
        "isPartOf": [
          {
            "id": "sg:journal.1043955", 
            "issn": [
              "1012-2443", 
              "1573-7470"
            ], 
            "name": "Annals of Mathematics and Artificial Intelligence", 
            "publisher": "Springer Nature", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "10-11", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "89"
          }
        ], 
        "keywords": [
          "Bayesian network", 
          "new learning algorithm", 
          "conditional independence tests", 
          "copula Bayesian network", 
          "learning algorithm", 
          "PC algorithm", 
          "continuous Bayesian networks", 
          "network", 
          "algorithm", 
          "independence test", 
          "better results", 
          "general model", 
          "task", 
          "learning", 
          "complexity", 
          "discrete case", 
          "parametric methods", 
          "model", 
          "constraints", 
          "multivariate distributions", 
          "copula function", 
          "order", 
          "model assumptions", 
          "data", 
          "method", 
          "et al", 
          "continuous variables", 
          "assumption", 
          "results", 
          "function", 
          "cases", 
          "variables", 
          "test", 
          "distribution", 
          "al", 
          "problem", 
          "high-dimensional multivariate distributions", 
          "Elidan", 
          "Bouezmarni et al", 
          "non-parametric continuous bayesian networks"
        ], 
        "name": "Constraint-based learning for non-parametric continuous bayesian networks", 
        "pagination": "1035-1052", 
        "productId": [
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1139150590"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/s10472-021-09754-2"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1007/s10472-021-09754-2", 
          "https://app.dimensions.ai/details/publication/pub.1139150590"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2022-01-01T19:02", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-springernature-scigraph/baseset/20220101/entities/gbq_results/article/article_909.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://doi.org/10.1007/s10472-021-09754-2"
      }
    ]
     

    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s10472-021-09754-2'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s10472-021-09754-2'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s10472-021-09754-2'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s10472-021-09754-2'


     

    This table displays all metadata directly associated to this object as RDF triples.

    137 TRIPLES      22 PREDICATES      71 URIs      57 LITERALS      6 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/s10472-021-09754-2 schema:about anzsrc-for:08
    2 anzsrc-for:0801
    3 schema:author N95cccf68655145298bfee04e6793c6cb
    4 schema:citation sg:pub.10.1007/3-540-36127-8_35
    5 sg:pub.10.1007/978-1-4613-0303-9_33
    6 sg:pub.10.1007/978-3-319-11259-6_64-1
    7 sg:pub.10.1007/978-3-642-12465-5_4
    8 sg:pub.10.1007/978-3-642-59365-9_8
    9 sg:pub.10.1007/978-3-642-93437-7_28
    10 schema:datePublished 2021-06-26
    11 schema:datePublishedReg 2021-06-26
    12 schema:description Modeling high-dimensional multivariate distributions is a computationally challenging task. In the discrete case, Bayesian networks have been successfully used to reduce the complexity and to simplify the problem. However, they lack of a general model for continuous variables. In order to overcome this problem, Elidan (2010) proposed the model of copula Bayesian networks that parametrizes Bayesian networks using copula functions. We propose a new learning algorithm for this model based on a PC algorithm and a conditional independence test proposed by Bouezmarni et al. (2009). This test being non-parametric, no model assumptions are made allowing it to be as general as possible. This algorithm is compared on generated data with the parametric method proposed by Elidan (2010) and proves to have better results.
    13 schema:genre article
    14 schema:inLanguage en
    15 schema:isAccessibleForFree true
    16 schema:isPartOf N9daa6850906749d193db6d655c10ad0a
    17 Ne03572316ae44b2e8e7189e8f00b0ea6
    18 sg:journal.1043955
    19 schema:keywords Bayesian network
    20 Bouezmarni et al
    21 Elidan
    22 PC algorithm
    23 al
    24 algorithm
    25 assumption
    26 better results
    27 cases
    28 complexity
    29 conditional independence tests
    30 constraints
    31 continuous Bayesian networks
    32 continuous variables
    33 copula Bayesian network
    34 copula function
    35 data
    36 discrete case
    37 distribution
    38 et al
    39 function
    40 general model
    41 high-dimensional multivariate distributions
    42 independence test
    43 learning
    44 learning algorithm
    45 method
    46 model
    47 model assumptions
    48 multivariate distributions
    49 network
    50 new learning algorithm
    51 non-parametric continuous bayesian networks
    52 order
    53 parametric methods
    54 problem
    55 results
    56 task
    57 test
    58 variables
    59 schema:name Constraint-based learning for non-parametric continuous bayesian networks
    60 schema:pagination 1035-1052
    61 schema:productId N64ab5909eddc4f7e873b5b8374fc5da1
    62 Nb83ac4a15b734b2890d45b33ef8db7d4
    63 schema:sameAs https://app.dimensions.ai/details/publication/pub.1139150590
    64 https://doi.org/10.1007/s10472-021-09754-2
    65 schema:sdDatePublished 2022-01-01T19:02
    66 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    67 schema:sdPublisher Nd5185ead0d194fada49bf94f75773a99
    68 schema:url https://doi.org/10.1007/s10472-021-09754-2
    69 sgo:license sg:explorer/license/
    70 sgo:sdDataset articles
    71 rdf:type schema:ScholarlyArticle
    72 N2076e64dc22a4b99ac56b6dfae0a079e rdf:first N84e7cd09e94348dbbbc52e48d732c521
    73 rdf:rest N9ac144c4017c4b76b7b79c76f57521ca
    74 N64ab5909eddc4f7e873b5b8374fc5da1 schema:name dimensions_id
    75 schema:value pub.1139150590
    76 rdf:type schema:PropertyValue
    77 N84e7cd09e94348dbbbc52e48d732c521 schema:affiliation grid-institutes:None
    78 schema:familyName Lebrun
    79 schema:givenName Régis
    80 rdf:type schema:Person
    81 N95cccf68655145298bfee04e6793c6cb rdf:first Ne021635a680e4a87b042980e99627993
    82 rdf:rest N2076e64dc22a4b99ac56b6dfae0a079e
    83 N9ac144c4017c4b76b7b79c76f57521ca rdf:first sg:person.010323340640.84
    84 rdf:rest rdf:nil
    85 N9daa6850906749d193db6d655c10ad0a schema:issueNumber 10-11
    86 rdf:type schema:PublicationIssue
    87 Nb83ac4a15b734b2890d45b33ef8db7d4 schema:name doi
    88 schema:value 10.1007/s10472-021-09754-2
    89 rdf:type schema:PropertyValue
    90 Nd5185ead0d194fada49bf94f75773a99 schema:name Springer Nature - SN SciGraph project
    91 rdf:type schema:Organization
    92 Ne021635a680e4a87b042980e99627993 schema:affiliation grid-institutes:grid.462844.8
    93 schema:familyName Lasserre
    94 schema:givenName Marvin
    95 rdf:type schema:Person
    96 Ne03572316ae44b2e8e7189e8f00b0ea6 schema:volumeNumber 89
    97 rdf:type schema:PublicationVolume
    98 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
    99 schema:name Information and Computing Sciences
    100 rdf:type schema:DefinedTerm
    101 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
    102 schema:name Artificial Intelligence and Image Processing
    103 rdf:type schema:DefinedTerm
    104 sg:journal.1043955 schema:issn 1012-2443
    105 1573-7470
    106 schema:name Annals of Mathematics and Artificial Intelligence
    107 schema:publisher Springer Nature
    108 rdf:type schema:Periodical
    109 sg:person.010323340640.84 schema:affiliation grid-institutes:grid.462844.8
    110 schema:familyName Wuillemin
    111 schema:givenName Pierre-Henri
    112 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010323340640.84
    113 rdf:type schema:Person
    114 sg:pub.10.1007/3-540-36127-8_35 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023803717
    115 https://doi.org/10.1007/3-540-36127-8_35
    116 rdf:type schema:CreativeWork
    117 sg:pub.10.1007/978-1-4613-0303-9_33 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008061296
    118 https://doi.org/10.1007/978-1-4613-0303-9_33
    119 rdf:type schema:CreativeWork
    120 sg:pub.10.1007/978-3-319-11259-6_64-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084933692
    121 https://doi.org/10.1007/978-3-319-11259-6_64-1
    122 rdf:type schema:CreativeWork
    123 sg:pub.10.1007/978-3-642-12465-5_4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031567327
    124 https://doi.org/10.1007/978-3-642-12465-5_4
    125 rdf:type schema:CreativeWork
    126 sg:pub.10.1007/978-3-642-59365-9_8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023644261
    127 https://doi.org/10.1007/978-3-642-59365-9_8
    128 rdf:type schema:CreativeWork
    129 sg:pub.10.1007/978-3-642-93437-7_28 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012429812
    130 https://doi.org/10.1007/978-3-642-93437-7_28
    131 rdf:type schema:CreativeWork
    132 grid-institutes:None schema:alternateName Airbus AI Research, 22 rue du Gouverneur Général Eboué, 92130, Issy les Moulineaux, France
    133 schema:name Airbus AI Research, 22 rue du Gouverneur Général Eboué, 92130, Issy les Moulineaux, France
    134 rdf:type schema:Organization
    135 grid-institutes:grid.462844.8 schema:alternateName Sorbonne Université- LIP6, 4 place Jussieu, 75005, Paris, France
    136 schema:name Sorbonne Université- LIP6, 4 place Jussieu, 75005, Paris, France
    137 rdf:type schema:Organization
     




    Preview window. Press ESC to close (or click here)


    ...