A New Ensemble Learning Algorithm Combined with Causal Analysis for Bayesian Network Structural Learning

Learning Bayesian Network Structure using Markov Blanket in K2 Algorithm

‎A Bayesian network is a graphical model that represents a set of random variables and their causal relationship via a Directed Acyclic Graph (DAG)‎. ‎There are basically two methods used for learning Bayesian network‎: ‎parameter-learning and structure-learning‎. ‎One of the most effective structure-learning methods is K2 algorithm‎. ‎Because the performance of the K2 algorithm depends on node...

A Bayesian Network Model of Causal Learning

Associationist theories of causal induction model learning as the acquisition of associative weights between cues and outcomes. An important deficit of this class of models is its insensitivity to the causal role of cues. A number of recent experimental findings have shown that human learners differentiate between cues that represent causes and cues that represent effects. Our Bayesian network ...

A New Polynomial Time Algorithm for Bayesian Network Structure Learning

We propose a new algorithm called SCD for learning the structure of a Bayesian network. The algorithm is a kind of constraintbased algorithm. By taking advantage of variable ordering, it only requires polynomial time conditional independence tests and learns the exact structure theoretically. A variant which adopts the Bayesian Dirichlet scoring function is also presented for practical purposes...

Sentiment analysis: Bayesian Ensemble Learning

Bayesian Ensemble Learning for Nonlinear Factor Analysis

An active research topic in machine learning is the development of model structures which would be rich enough to represent relevant aspects of the observations but would still allow efficient learning and inference. Linear factor analysis and related methods such as principal component analysis and independent component analysis are widely used feature extraction and data analysis techniques. ...