USA | SINGAPORE
The Bayesian network paradigm, a mathematical formalism for knowledge representation and modeling.
BEKEE, the Bayesia Expert Knowledge Elicitation Environment
Bayesia Market Simulator
Bayesia Engine API
BayesiaLab is built on the foundation of the Bayesian network formalism (perhaps in the same way as a spreadsheet program would be based on arithmetics).
BayesiaLab can generate Bayesian networks from human knowledge and/or by machine learning from data. The Bayesian network thus become as compact model of the underlying - and often high-dimensional - problem domain.
Based on this network model, BayesiaLab provides a wide range of analysis, simulation and optimization functions that allow the researcher to exploit all the dynamics captured in the network.
Data Science, Applied Research, Market Research, Bioinformatics, Engineering, Risk Analysis, Decision Science, Marketing Science, Econometrics, Knowledge Management
A comprehensive software suite for employing Bayesian networks for research and analytics.
BayesiaLab 5.2 is a powerful desktop application with a highly sophisticated graphical user interface, which provides scientists a comprehensive “lab” environment for machine learning, knowledge modeling, diagnosis, analysis, simulation, and optimization.
With the launch of BayesiaLab 1.0 in 2001, using Bayesian networks has become practically feasible for applied researchers, enabling them to gain deep understanding of high-dimensional domains. BayesiaLab leverages the inherently graphical structure of Bayesian networks for exploring and explaining complex problems. Also, among all analytics software packages, BayesiaLab is unique in its ability to formally distinguish between observational and causal inference.
A Bayesian network is a type of a mathematical model that can simultaneously represent manifold relationships between variables in a system. The graph of a Bayesian network contains nodes (representing variables) and directed arcs that link the nodes. The arcs quantify the relationships of the nodes.
Whereas traditional statistical models are of the form y=f(x), Bayesian networks do not have to distinguish between independent and dependent variables. Rather, a Bayesian network approximates the entire joint probability distribution of the system under study.
This allows the researcher to carry out "omnidirectional inference," i.e. to reason from cause to effect (simulation), or from effect to cause (diagnosis), all within the same model.
For North America
For Asia Pacific