BayesiaLab is a 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 practically apply artificial intelligence tools, thus transforming and, more importantly, massively accelerating research workflows.
Analysts and researchers around the world, including our strategic partner P&G, have embraced BayesiaLab to gain unprecedented insights into problems, which had previously not been tractable with traditional statistical methods.
The latest version of BayesiaLab, 5.1, is the result of nearly twenty years of development by a team of researchers, Dr. Lionel Jouffe and Dr. Paul Munteanu, who are now widely recognized as world leaders in their field of study.
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 application today are still characterized by arcane commands, BayesiaLab users can navigate with ease through menus and wizards to use all the powerful features. They can thus focus on their research without having to worry about idiosyncratic syntax.
The following list provides a glimpse of the wide-ranging functionalities provided by BayesiaLab:
- Creating and editing Bayesian Networks based on expert knowledge
- Combining experts’ knowledge with machine learning
- Automatic modeling through data mining (supervised and unsupervised), i.e. learning Bayesian networks from existing data
- Data classification and clustering
- Probabilistic structural equation modeling for driver analysis
- Sensitivity analysis of evidence and parameters
- Target node optimization
- Intervention analysis, e.g. analysis of policy impact
- Direct Effects Analysis with Likelihood Matching
- Contribution Analysis based on counterfactuals
- Missing values imputation
- Multi-quadrant analysis
- Market share simulation with Bayesia Market Simulator (separate subscription required)
- Resource Allocation Optimization, e.g. for marketing mix modeling
- Forecasting with temporal Bayesian networks
- Automated Conditional Mean Analysis
- Range of discretization algorithms incl. Density Approximation, K-Means, Equal Distance, Equal Frequencies, etc.
For more details about licensing options and pricing, please see Pricing Information.