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Be aware of trends and take the right decisions with BayesiaLab
“BayesiaLab offers all the strengths of modern technology, while being easy to use. It is a particularly interesting tool for statisticians.”
“BayesiaLab provides exceptional capability in probabilistic inference. This Bayesian network software allows model building based on data, expert knowledge or any combination of the two. It polishes off modeling with a suite of advanced analysis methods unavailable in other such tools. The results are clear, interpretable solutions of the problem at hand. With BayesiaLab, Bayesia has set new standards of usability, productivity and value for Bayesian network software.”
BayesiaLab technology, a system capable of exploring uncertainty.
Ergonomic and easy to use, BayesiaLab provides concrete lines of thought, adapted to decision maker’s problems, even if the database has missing values and censored states.
BayesiaLab enhances links and interdependency between each factor of the one same problem. The impact of each factor can be quantified on the whole of the system.
With BayesiaLab, the decision maker himself develops various scenarios by changing the key factors of the problem addressed. No cause is overlooked and all possibilities are considered.
BayesiaLab is the ideal tool for analyzing the uses and attitudes of a group of customers, satisfaction questionnaire analysis, brand image analysis, segmentation (creation of types) of customers or products, assessment of appetence scores in relation to new products, etc.
Find out more about BayesiaLab and its features »
Examples of applications
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Analysis of a perfume market
Applibugs 2009
Application of Probabilistic Structural Equations (PSE) to the analysis of a perfume market
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Identifying drivers of liking
Pangborn 2009
Presentation at Pangborn 2009 conference of a method to identify drivers of liking for fine fragrances with BayesiaLab (presentation by our partner Repères)
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Studying consumer drivers
Sensometrics meeting 2008 (St Catharine, Ontario)
Sensometrics meeting (St Catharine, Ontario, July 21st 2008) : studying consumer drivers with bayesian networks (in collaboration with Repères).
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Consumer segmentation
SKIM Conference 2008 (Barcelone)
Presentation at the SKIM conference (Barcelona, may 2008) of an innovative method for consumer segmentation through Bayesian networks (in collaboration with Repères).
- Customer characterization, development of profiles Customer profile learning from a database and characterization in order predict suitable products and detect fraud.
- Satisfaction questionnaire analysis BayesiaLab examines customer satisfaction with a fine tooth comb.
- Implementation of a Bayesian score Implementation of a Bayesian score and use of a graphic module to view the data.
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