Unsupervised Structural Learning Algorithms

Unsupervised Structural Learning Algorithms

In statistics, unsupervised learning is typically understood to be a classification or clustering task. To make a very clear distinction, we put emphasis on “structural” in “Unsupervised Structural Learning,” which covers a number of important algorithms in BayesiaLab.

Unsupervised Structural Learning means that BayesiaLab can discover probabilistic relationships between a large number of variables, without the need to define inputs or outputs. One might say that this is the quintessential form of knowledge discovery as no assumptions whatsoever are required to perform these algorithms on unknown datasets.

Available Algorithms:

  • Maximum Weight Spanning Tree
  • EQ
  • SopLEQ
  • Taboo Search
  • Taboo Order

Relevant Case studies :


Supervised Learning Algorithms

Supervised Learning in BayesiaLab has the same objective as many traditional modeling techniques, i.e. to develop a model for predicting a target variable. Some other data mining packages also offer “Bayesian Networks” as an option in their array of available techniques. However, in most cases, these packages are restricted in their capabilities to a very limited type of network, i.e. the Naïve Bayesian Network.

Within BayesiaLab, a vastly greater number of algorithms is available to search for a Bayesian network that best describes the target variable, while taken into account the complexity of the resulting network. The Markov Blanket algorithm should be highlighted here as its speed is particularly helpful whenever dealing with a larger number of variables. In this context, the Markov Blanket also serves as an exceptionally powerful variable selection algorithm.

Available Algorithms:

  • Naïve
  • Augmented Naïve
  • Tree Augmented Naïve
  • Sons & Spouses
  • Markov Blanket
  • Augmented Markov Blanket
  • Minimal Augmented Markov Blanket
  • Semi-Supervised Learning

Relevant Tutorials:



Clustering in BayesiaLab covers both data clustering (e.g. by observations) and variable clustering, which, as the name implies, allows the grouping of variables according to the strength of their mutual relationships.

A third variation of this concept is of particular importance in BayesiaLab: the semi-automatic Multiple Clustering workflow can be described as a kind of nonlinear, nonparametric and nonorthogonal factor analysis.

Relevant Case studies :




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