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Advanced BayesiaLab Course 
60-Day Self-Study Edition

 

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Advanced BayesiaLab Course —
60-Day Self-Study Edition

Given the near-universal restrictions on hosting events at the moment, we have expanded the training options of the BayesiaLab Academy beyond the classroom setting.

In addition to many hours of free webinar and seminar recordings, you can now participate in our popular three-day courses entirely offline, in your time zone, and at your own pace. 

20+ Hours of Video 

This new self-study program precisely mirror the three-day advanced course course taught by Dr. Lionel Jouffe. It includes over 20 hours of lectures plus software demos recorded directly from the instructor's screen in 1080P resolution! Additionally, you'll have access to over 250 pages of course notes. 

60 Days of Access to the BayesiaLab Education Edition

The self-study course includes a 60-day license to the Education Edition of BayesiaLab. All study materials, including videos and slides, are exclusively accessible from within the BayesiaLab software during the license period.

Ask the Experts Anytime

While you obviously can't ask questions as you watch a recorded lecture, you can still connect with our instructors anytime. We are available via chat, email, phone, social media, and, of course, via our new BayesiaLab Community

Purchase the Advanced Course

Take your BayesiaLab certification to the next level by completing this advanced program! This course gives you an in-depth view of what you can do with Bayesian networks as a research practitioner. In the advanced course, we study those topics in more detail that we only cover superficially during the introductory course.

Course Topics

  • Expert-Based Modeling with BEKEE
  • Discretization of Continuous Variables
  • Synthesis of New Variables (Manual Synthesis and Data Clustering)
  • Fine-Tuning of Learning Algorithms
  • Network Quality Evaluation
  • Target Optimization
  • Parameter Sensitivity Analysis
  • Function Nodes
  • Influence Diagrams
  • Dynamic Bayesian Networks
  • Bayesian Updating
  • Aggregation of the Discrete States
  • Missing Values Processing
  • Credible/Confidence Intervals Analysis
  • Evidence Analysis
  • Function Optimization
  • Contribution Analysis

Participants in the Advanced Course are required to have completed the Introductory Course on a previous date (see course calendar).

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