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
- 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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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
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