What is BayesiaLab?
Knowledge Discovery
Automatic Omnidirectional Inference
Causal Inference
Intuitive Interpretation
Superb User Interface

BayesiaLab is a very intuitive and extremely powerful desktop application (Windows/Mac/Unix) for knowledge discovery, data mining, analytics, predictive modeling and simulation - all based on the paradigm of Bayesian networks. Bayesian networks have become a very powerful tool for deep understanding of very complex, high-dimensional problem domains, ranging from bioinformatics to marketing science.

BayesiaLab is the world's only comprehensive software package for learning, editing and analyzing Bayesian networks. It provides perhaps the easiest way to leverage recent innovations in artificial intelligence for real-world research and analytics. Read More
BayesiaLab is unique in its ability to perform unsupervised structural learning. That means you can use BayesiaLab to machine-learn a network structure from data, without any prior knowledge of the domain. Read More
Inference with BayesiaLab is "omnidirectional", meaning that no distinction between independent and dependent needs to be made when creating network model. Upon observing data, i.e. setting evidence on one or more nodes, BayesiaLab automatically performs inference across all nodes in the network, in all directions. Read More
With BayesiaLab, you can use a Bayesian Network to perform both statistical (observational) and causal inference. This allows you to correctly simulate an intervention in a domain and thus anticipate the consequences of actions not yet taken. Read More
Constructing and interpreting Bayesian networks is highly intuitive. No statistical knowledge is required to understand how they work, which facilitates communication with a large audience. Stakeholders can easily recognize the parts of the network that reflects their domain knowledge. As no arcane formulas are needed to explain the dynamics of a system, researchers can much more easily get "buy-in" for their analysis.

In addition to the inherently visual nature of Bayesian networks, BayesiaLab offers a myriad of visualization features and layout algorithms, so you can transform your insights quickly and easily into compelling presentations. Read More
Beyond its analytic capabilities, BayesiaLab provides an extremely user-friendly interface, which allows new novices and experts alike to easily use the myriad of functions available in the program. While many statistical applications today are still characterized by arcane commands, you can navigate with ease through BayesiaLab's menus. Numerous wizards help you to use all its powerful features in the right context. It's as if BayesiaLab anticipates the next question in your research workflow. You can thus focus on your research topic without having to worry about idiosyncratic syntax. Read More

BayesiaLab Course in Chicago, July 9-11, 2013

Learn the foundations of Bayesian Networks and how to use them as a practical research framework with the BayesiaLab software platform. Join us for this 3-day course, July 9 through 11, in Downtown Chicago.

Course Venue: 12 East Ohio Street, Suite 300, Chicago, IL 60611 (River North Area, off North Michigan Avenue)

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Audience: Applied researchers, statisticians, data scientists, data miners, epidemiologists, predictive modelers, econometricians, economists, market researchers, knowledge managers, marketing scientists, students and teachers in related fields.

Level: The course will be taught at a beginner level, so no prior knowledge of Bayesian networks is necessary. However, undergraduate-level familiarity with probability theory and statistics is recommended.

Objective: Completing the course as a Certified BayesiaLab Analyst and becoming proficient in using Bayesian networks for a broad range of applied research and analytics tasks.

BayesiaLab Course in Bangalore, September 11-13

DCAL@IIMB After a completely sold-out program in Bangalore this past February, we will be returning to India with more BayesiaLab courses in the fall!

Learn the foundations of Bayesian Networks and how to use them as a practical research framework with the BayesiaLab software platform in this 3-day course at the Data Centre and Analytics Lab at the Indian Institute of Management Bangalore (DCAL@IIMB).

Add to Calendar

Audience: Applied researchers, statisticians, data scientists, data miners, epidemiologists, predictive modelers, econometricians, economists, market researchers, knowledge managers, marketing scientists, students and teachers in related fields.

Level: The course will be taught at a beginner level, so no prior knowledge of Bayesian networks is necessary. However, undergraduate-level familiarity with probability theory and statistics is recommended.

Objective: Completing the course as a Certified BayesiaLab Analyst and becoming proficient in using Bayesian networks for a broad range of applied research and analytics tasks.