DirectLiNGAM: A direct method for learning a linear non-gaussian structural equation model

Journal of Machine Learning Research

Volumn 12, Issue , 2011, Pages 1225-1248

  Shimizu, Shohei ^a   Inazumi, Takanori ^a   Sogawa, Yasuhiro ^a   Hyvärinen, Aapo ^b   Kawahara, Yoshinobu ^a   Washio, Takashi ^a   Hoyer, Patrik O ^b   Bollen, Kenneth ^c  

^a OSAKA UNIVERSITY   (Japan)

^b UNIVERSITY OF HELSINKI   (Finland)

^c UNIVERSITY OF NORTH CAROLINA   (United States)

Author keywords

Bayesian networks; Causal discovery; Independent component analysis; Non Gaussianity; Structural equation models

Indexed keywords

ALGORITHMIC PARAMETERS; CAUSAL DISCOVERY; CAUSAL ORDERING; CAUSAL RELATIONS; CONNECTION STRENGTH; CONTINUOUS VARIABLES; CONVENTIONAL METHODS; CORRECT SOLUTION; DIRECT METHOD; ESTIMATION METHODS; FINITE NUMBER; FIXED NUMBERS; MODEL ASSUMPTIONS; NETWORK STRUCTURES; NON-GAUSSIAN; NONGAUSSIANITY; PRIOR KNOWLEDGE; SAMPLE SIZES; SEARCH ALGORITHMS; STRUCTURAL EQUATION MODELS;

BAYESIAN NETWORKS; BEHAVIORAL RESEARCH; BLIND SOURCE SEPARATION; DISTRIBUTED PARAMETER NETWORKS; INDEPENDENT COMPONENT ANALYSIS; INFERENCE ENGINES; INTELLIGENT NETWORKS; TELECOMMUNICATION SERVICES;

MATHEMATICAL MODELS;

EID: 79955829373     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Article

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