New Release: BayesiaLab 9
Since 2001, BayesiaLab has been the undisputed reference standard for Bayesian network software. Version 9 raises the bar even higher by further expanding the range of research and analytics applications of the Bayesian Network formalism. With BayesiaLab 9, the powerful properties of the Bayesian Network paradigm can be utilized even better for exploring complex, high-dimensional problem domains.
New & Updated Features & Functions
Here is a small selection of new or updated features released in BayesiaLab 9:
- 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 can 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.
For a complete documentation of all new features, please see the BayesiaLab Library.
Introducing BayesiaLab 9
Bayesia's CEO and co-founder, Dr. Lionel Jouffe, explained some of the innovations of BayesiaLab 9 in his presentation at the 7th Annual BayesiaLab Conference in Durham, October 10–11, 2019.
Note to Current BayesiaLab Users
Existing users will be prompted to download the new version when they start up their current version of BayesiaLab (5.4 or higher). Alternatively, you can check for available updates via the Help menu in BayesiaLab.
The upgrade is free for all rented/term licenses and for perpetual licenses with a current Technical Support subscription. If your subscription has expired, please contact us via email at firstname.lastname@example.org so we can let you know about renewal options.
Please note that xbl files saved with BayesiaLab 9 cannot be opened with earlier versions of BayesiaLab.
Note to New Users
For those new to BayesiaLab, you can apply for a 30-day evaluation version by registering here: BayesiaLab 9 Evaluation. If you've already tried BayesiaLab in the past, we invite you to start a fresh evaluation.