Learning possibilistic causal model from data with transformation from probability into possibility

被引：0
|
作者：
Department of Management and Information Systems Science, Nagaoka University of Technology, 1603-1, Kami-tomioka, Nagaoka, Niigata 940-2188, Japan [1 ]
机构：
来源：
WSEAS Trans. Inf. Sci. Appl. | 2006年 / 10卷 / 1885-1892期
关键词：
21;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
引用
收藏
相关论文
共 50 条
  • [31] Tychomancy: Inferring Probability from Causal Structure
    Landes, Juergen
    International Studies in the Philosophy of Science, 2014, 28 (04) : 446 - 448
  • [32] An Extended Approach to Learning Recursive Probability Trees from Data
    Cano, Andres
    Gomez-Olmedo, Manuel
    Perez-Ariza, Cora B.
    INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, 2015, 30 (03) : 355 - 383
  • [33] Some Examples of Computing the Possibilistic Correlation Coefficient from Joint Possibility Distributions
    Fuller, Robert
    Mezei, Jozsef
    Varlaki, Peter
    COMPUTATIONAL INTELLIGENCE IN ENGINEERING, 2010, 313 : 153 - +
  • [34] Transformation Of Arabic Learning From Classical Model To Digital Model
    Rini
    Mustofa, Arif
    Kurniawan, Rahadian
    IJAZ ARABI JOURNAL OF ARABIC LEARNING, 2022, 5 (03): : 892 - 902
  • [35] THE ROLE OF MODEL SELECTION IN CAUSAL INFERENCE FROM NONEXPERIMENTAL DATA
    ROBINS, JM
    GREENLAND, S
    AMERICAN JOURNAL OF EPIDEMIOLOGY, 1986, 123 (03) : 392 - 402
  • [36] Towards a causal model from pipeline incident data analysis
    Halim, S. Zohra
    Yu, Mengxi
    Escobar, Harold
    Quddus, Noor
    Kay, Mary
    PROCESS SAFETY AND ENVIRONMENTAL PROTECTION, 2020, 143 : 348 - 360
  • [37] Learning possibilistic dynamic systems from state transitions
    Hu, Hongbo
    Wang, Yisong
    Inoue, Katsumi
    FUZZY SETS AND SYSTEMS, 2025, 504
  • [38] Extracting causal graphs from an open provenance data model
    Miles, Simon
    Groth, Paul
    Munroe, Steve
    Jiang, Sheng
    Assandri, Thibaut
    Moreau, Luc
    CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE, 2008, 20 (05): : 577 - 586
  • [39] Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data
    Lowe, Sindy
    Madras, David
    Zemel, Richard
    Welling, Max
    CONFERENCE ON CAUSAL LEARNING AND REASONING, VOL 177, 2022, 177
  • [40] Learning causal Bayesian networks from incomplete observational data and interventions
    Borchani, Hanen
    Chaouachi, Maher
    Ben Amor, Nahla
    SYMBOLIC AND QUANTITATIVE APPROACHES TO REASONING WITH UNCERTAINTY, PROCEEDINGS, 2007, 4724 : 17 - +
12345