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The Bayesia portfolio of research software is the result of over 20 years of continuous research and development by two French professors in the field of artificial intelligence, Dr. Lionel Jouffe and Dr. Paul Munteanu. Their team of computer scientists and software developers at Bayesia S.A.S. has embraced the Bayesian networks paradigm and built tools for making it accessible to a broad audience, and practical for a wide range of research tasks.
The idea of Bayesian networks dates back to the mid-1980s, when Professor Judea Pearl of UCLA began to formalize their semantics in a series of seminal works. The study of Bayesian networks has since grown into a large body of work with dozens of books and countless scientific papers exploring all their properties.
However, thanks to Bayesia’s software tools, and the ever-increasing power of computers, Bayesian networks have become powerful and practical tool well beyond the world of academia. For applied research in all domains, Bayesian networks can facilitate deep understanding of very complex, high-dimensional problem domains. Their computational efficiency and inherently visual structure make Bayesian networks attractive for exploring and explaining complex domain. Most importantly, Bayesian networks allow reasoning about such domains in a formally correct yet highly intuitive way.