Resources
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Tutorials
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User guide
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Case studies
- Production optimisation through feedback analysis
- Satisfaction questionnaire analysis
- Football World Cup 2010
- Health trajectory analysis
- Biocomputing transcriptome analysis
- Risk analysis and security policy
- Difficult intubations analysis
- Cyber-crimes detection
- Customer characterization
- Organization name identification
- Implementation of a Bayesian score
- Modeling and simulation of dynamic systems
- Analysis of a perfume market
- Consumer segmentation
- Identifying drivers of liking
- Salmonella isolation
- Political Analysis
- Studying consumer drivers
- Employees satisfaction analysis
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Resources
BayesiaLab in action: presentations and case studies
Software presentations
- Bayesialab's introductory presentation Discover the world of the Bayesian networks with this first presentation of BayesiaLab.
- Analysis toolbox Bayesian networks are naturally legibles. BayesiaLab goes further with its very complete analysis toolbox.
- Bayesian network learning BayesiaLab is a powerful data mining tool that allows easily and simply discovering the knowledge hidden in your databases.
- Decision support Use of Decision and Utility nodes to discover the optimal action policies on a simple example: drilling of oil wells.
- Dynamic bayesian networks and action policy learning All the Bayesian networks used so far were static probabilistic models. BayesiaLab allows too representing dynamic models. By exploiting the power of Bayesian networks, it is possible to calculate your optimal action policies.
- Filtered/Censored States In a lot of studies, one needs to use variables that are contingent on context, i.e. variables that only exist depending on the values of other variables (usage, configuration). We describe here how BayesiaLab takes rigorously these variables into account during learning and analysis
- Probabilistic structural equations We describe in this presentation how BayesiaLab can be used to use Bayesian networks as a pragmatic alternative to Structural Equation Modeling, PLS and Path Analysis.
- BayesiaLab Knowledge Elicitation Environment This presentation describes the new BayesiaLab 5.0 Knowledge Elicitation Environment. This environment allows reducing biases (cognitive, group and facilitator), and allows to greatly improve the traceability of the brainstorming session.
Watch 
Download 
Read User guide
Case studies
Industry
- Modeling and simulation of dynamic systems Optimisation of the maintenance strategies of a complex industrial process, through a dynamic bayesian network.
- Production optimisation through feedback analysis Through this case study, discover the power of BayesiaLab’s automatic learning process in exploiting and benefitting from the data obtained from feedback.
Marketing
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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.
Health
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Difficult intubations analysis
Scata 2007 (London)
Prediction of difficult intubation (DI) is crucial during pre anaesthesia assessment of a patient. Many criterions are used to predict DI with different performances. In this case study, we show how BayesiaLab, through its learning algorithms, allows to quickly discovering unknown probabilistic relationships between variables and enhance prediction.
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Salmonella isolation
20th International Pig Veterinary Society Congress, 2008, Durban (South Africa)
Identification of factors associated with Salmonella isolation on pork carcasses via bayesian networks.
- Biocomputing transcriptome analysis Bioinformatics with BayesiaLab.
- Health trajectory analysis Prediction of medical needs with BayesiaLab.
Strategy
- Football World Cup 2010 A funny game illustrating the chances of a team to qualify for the second round of the 2010 World Cup.
- Employees satisfaction analysis Application of probabilistic structural equations to find employees satisfaction drivers.
- Political Analysis Analysis of the French Presidential Elections of April 2007.
Risk management
- Risk analysis and safety policy: example of transporting people Risk analysis for transporting people; modelling of a prevention policy.
- Cyber-crimes detection
Others
- Organization name identification Text-mining with BayesiaLab.