The Leading Desktop Software for Bayesian Networks.
Artificial Intelligence for Research, Analytics, and Reasoning

Built on the foundation of the Bayesian network formalism, BayesiaLab is a powerful desktop application (Windows, macOS, Linux/Unix) with a highly sophisticated graphical user interface. It provides scientists a comprehensive “lab” environment for machine learning, knowledge modeling, diagnosis, analysis, simulation, and optimization. With BayesiaLab, it has become feasible for applied researchers in many fields, rather than just computer scientists, to take advantage of the Bayesian network formalism.

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Bayesian Expert System for Troubleshooting

BEST is a suite of enterprise-level stakeholder-specific modules for the design, administration and operational deployment of intelligent troubleshooting applications in complex technical areas. It provides expressive yet easy-to-use tools to model and disseminate expert knowledge, machine learning capabilities to automatically improve models using in-service experience, and powerful optimization algorithms to generate troubleshooting strategies that minimize the cost of diagnosis.

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Bayesian Representation and Inference for Complex Knowledge Structuring

BRICKS is a probabilistic relational modeling framework and technology platform based on an object-oriented extension of classical Bayesian networks. BRICKS improves modeling efficiency by reusing modeling patterns or classes and assembling elementary ‘bricks’, i.e. objects created from classes. BRICKS also enhances inference efficiency by utilizing symmetries and leveraging advanced decompositions of probabilistic dependencies.

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BRICKS Workflow

What is a Bayesian Network?

Invented by Judea Pearl in the 1980s at UCLA, Bayesian networks are a mathematical formalism that can simultaneously represent a multitude of probabilistic 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 represent 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.

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The BayesiaLab Workflow in Practice

Researchers can use BayesiaLab to encode their domain knowledge into a Bayesian network. Alternatively, BayesiaLab can machine-learn a network structure purely from data collected from the problem domain. Irrespective of the source, a Bayesian network becomes a representation of the underlying, often high-dimensional problem domain. The researcher can then use BayesiaLab to carry out “omni-directional inference,” i.e., reason from cause to effect (simulation), or from effect to cause (diagnosis), within the Bayesian network model.

On this basis, BayesiaLab offers an extensive analytics, simulation and optimization toolset, providing comprehensive support for policy development and decision making. In this context, BayesiaLab is unique in its ability to distinguish between observational and causal inference. Thus, decision makers can correctly simulate the consequences of actions not yet taken.

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The BayesiaLab Workflow
Bayesian Network