We have had to wait over 30 years since the naive Bayes model was first introduced in 1960
for the so-called Bayesian network classifiers to resurge. Based on Bayesian networks …

OBJECTIVE: As a complementary medical system to Western medicine, traditional Chinese
medicine (TCM) provides a unique theoretical and practical approach to the treatment of …

Two attributes $ A $ and $ B $ are said to interact when it helps to observe the attribute
values of both attributes together. This is an example of a $2 $-way interaction. In general, a …

OBJECTIVES: Bayesian networks (BNs) are rapidly becoming a leading technology in
applied Artificial Intelligence, with many applications in medicine. Both automated learning …

Empirically derived rainfall thresholds of debris flows are used for regional-scale early
warning. However, triggering rainfall intensities of post-seismic debris flows evolve with time …

Abstract Background The Naive Bayes (NB) classifier is a powerful supervised algorithm
widely used in Machine Learning (ML). However, its effectiveness relies on a strict …

In data analysis, latent variables play a central role because they help provide powerful
insights into a wide variety of phenomena, ranging from biological to human sciences. The …

Classification problems have a long history in the machine learning literature. One of the
simplest, and yet most consistently well-performing set of classifiers is the Naïve Bayes …

Sum-product networks (SPNs) are a promising and novel type of probabilistic model, which
has been receiving significant attention in recent years. There are, however, several open …

There is a commonly held opinion that the algorithms for learning unrestricted types of
Bayesian networks, especially those based on the score+ search paradigm, are not suitable …