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Go beyond descriptive analytics and enter the realm of probabilistic and causal reasoning with Bayesian networks. Learn all about designing and machine-learning Bayesian networks with BayesiaLab (see complete course program).

This highly acclaimed course gives you a comprehensive introduction that allows you to employ Bayesian networks for applied research across many fields, such a biostatistics, decision science, econometrics, ecology, marketing science, sensory research, sociology, just to name a few.

The hallmark of this three-day course is that every segment on theory is immediately followed by a corresponding practice session using BayesiaLab. Thus, you have the opportunity to implement on your computer what the instructor just presented in his lecture. This includes knowledge modeling, probabilistic reasoning, causal inference, machine learning, probabilistic structural equation models, plus many more examples. Given the strictly limited class size, the instructor is always available to coach you one-on-one as you progress through the exercises.

After the end of the course, you can continue your studies as you will have access to your training BayesiaLab license. Additionally, a workbook, plus numerous data sets and sample networks help you to experiment independently with Bayesian networks. To date, over 1,000 researchers from all over the world have taken this course (see testimonials). For most of them, Bayesian networks and BayesiaLab have become crucial tools in their research.

Learn the advanced knowledge modeling, machine learning and analysis methods with Bayesian networks and BayesiaLab.
Participants in the Advanced Course are required to have completed the Introductory Course.