Distance sampling with independent observers: Reducing bias from heterogeneity by weakening the conditional independence assumption

2021 ◽  
pp. 137-148
Author(s):  
J. Laake
Keyword(s):  
Conditional Independence ◽  
Distance Sampling ◽  
Independence Assumption ◽  
Conditional Independence Assumption

The role of the conditional independence assumption in statistically matching income and consumption

2016 ◽  
Vol 32 (4) ◽  
pp. 667-675 ◽  
Author(s):  
Gabriella Donatiello ◽  
Marcello D'Orazio ◽  
Doriana Frattarola ◽  
Antony Rizzi ◽  
Mauro Scanu ◽  
...  
Keyword(s):  
Conditional Independence ◽  
Independence Assumption ◽  
Conditional Independence Assumption

A novel approach to fully representing the diversity in conditional dependencies for learning Bayesian network classifier

2021 ◽  
Vol 25 (1) ◽  
pp. 35-55
Author(s):  
Limin Wang ◽  
Peng Chen ◽  
Shenglei Chen ◽  
Minghui Sun
Keyword(s):  
Bayesian Network ◽  
Conditional Independence ◽  
Structure Learning ◽  
Classification Performance ◽  
Data Sets ◽  
Independence Assumption ◽  
Bayesian Network Classifiers ◽  
Novel Approach ◽  
Conditional Independence Assumption ◽  
Dependence Criterion

Bayesian network classifiers (BNCs) have proved their effectiveness and efficiency in the supervised learning framework. Numerous variations of conditional independence assumption have been proposed to address the issue of NP-hard structure learning of BNC. However, researchers focus on identifying conditional dependence rather than conditional independence, and information-theoretic criteria cannot identify the diversity in conditional (in)dependencies for different instances. In this paper, the maximum correlation criterion and minimum dependence criterion are introduced to sort attributes and identify conditional independencies, respectively. The heuristic search strategy is applied to find possible global solution for achieving the trade-off between significant dependency relationships and independence assumption. Our extensive experimental evaluation on widely used benchmark data sets reveals that the proposed algorithm achieves competitive classification performance compared to state-of-the-art single model learners (e.g., TAN, KDB, KNN and SVM) and ensemble learners (e.g., ATAN and AODE).

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