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Bayesia S.A.S. is a French software development company, founded in 2001 by Dr. Lionel Jouffe and Dr. Paul Munteanu, which specializes in artificial intelligence technology. Learn More...

Bayesia's software portfolio focuses on all aspects of decision support with Bayesian networks and includes BayesiaLab, BEST, and BRICKS. Their spectrum of applications ranges from individual decision support to large-scale policy analysis and risk assessment of industrial systems.

BayesiaLab - Your Desktop Analytics and Research Laboratory

BayesiaLab is a powerful Artificial Intelligence software that provides scientists a comprehensive “lab” environment for machine learning, knowledge modeling, analytics, simulation, and optimization — all based on the Bayesian network paradigm.

BayesiaLab is a desktop application (Win/Mac/Unix), which features a highly intuitive graphical user interface. It is ideally suited for individual researchers or subject-matter experts who are exploring complex problem domains. Learn More...

 

BEST (Bayesian Expert System for Troubleshooting)

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The BEST suite consists of a set of software modules for the design, administration and operational deployment of intelligent troubleshooting applications for complex technical domains. It provides powerful and easy-to-use tools for modeling and disseminating expert knowledge. BEST features machine learning capabilities that can automatically improve models using in-service experience. Additionally, BEST can generate troubleshooting strategies that minimize the cost of performing diagnosis.

BEST is an enterprise-level application with stakeholder-specific modules for domain experts, service representatives, technicians, and even end-users. Learn More...

 

BRICKS (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. Learn More...