BayesiaLab 9.0: New Features & Updates (12/2019)

Here is a small selection of new or updated features released in BayesiaLab 9.0:

  • The Target and Function node optimization tools are enhanced with new options and outputs.
  • The new  Most Relevant Explanations function provides precise and concise explanations for your current set of evidence.
  • By setting Structural Priors, you incorporate any available, partial prior knowledge about a structure.
  • You can improve the quality of machine-learned models with BayesiaLab's new Smoothed Bootstrapping algorithm, Data Perturbation. It perturbs the sample data not only with the weight of each particle but also with the overall Structural Coefficient.
  • You can automatically estimate Structural Priors via Resampling/Bagging. This is particularly powerful for small data sets as you no longer have to search for the best Structural Coefficient. 
  • As a step toward learning causal Bayesian networks, you can induce a Partial Order among your variables via Resampling/Bagging.
  • The Markov Blanket Learning Algorithms can now take into account constraints on arc directionality as defined by Temporal Indices or Forbidden Arcs.
  • The cross-validation of Variable Clustering now features Purities to estimate to quality of the Factors.
  • The Code Export function (optional), can now produce Python code. This code, when embedded into your own program, can compute the posterior probability of a Target node given its Markov Blanket.
  • By separate subscription, a new Media tool gives you access to presentation slides and recorded videos of the 3-Day Introductory and Advanced BayesiaLab Courses.
  • In the 3D Mapping tool, you can now apply textures to nodes and use auto-rotate for creating visually appealing animations of your Bayesian network models.

BayesiaLab 9.0 presented at the 7th BayesiaLab Conference

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