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A multivariate additive noise model for complete causal discovery
Pramod Kumar Parida
,
Tshilidzi Marwala
, Snehashish Chakraverty
Faculty of Engineering and the Built Environment
University of Johannesburg
National Institute of Technology Rourkela
Research output
:
Contribution to journal
›
Article
›
peer-review
11
Citations (Scopus)
Overview
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Mathematics
Additive Noise
100%
Bivariate
20%
Causal Discovery
100%
Causal Model
100%
Causal Relation
20%
Causal Structure
20%
Comparison Test
20%
Directed Acyclic Graph
40%
External Influence
20%
Gaussian Distribution
60%
Gaussian Mixture Model
20%
Identifiability
20%
Independent Component
40%
Nonlinear
60%
Keyphrases
Additive Noise Model
100%
Bivariate Model
20%
Casual Inference
20%
Causal Discovery
100%
Causal Independence
20%
Causal Influence
40%
Causal Structure
20%
Comparison Test
20%
Correlated Features
20%
Feature Dependency
40%
Feature Relation
20%
Graph Regression
20%
Graph Search
20%
Multi-feature
40%
Multi-nodal
20%
Multivariate System
20%
Nodal Structure
20%
Real World Phenomena
20%
Uncorrelated Feature
20%
Computer Science
Causal Influence
100%
Directed Acyclic Graph
100%
Gaussian Mixture Model
50%
Independent Component Analysis
100%
Influence Factor
100%
Performance Test
50%
World Phenomenon
50%
Neuroscience
Causal Model
100%
Independent Component Analysis
40%
Biochemistry, Genetics and Molecular Biology
Gaussian Distribution
100%