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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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.
Here are the main steps of the workflow:
- Unsupervised learning to discover the direct probabilistic relations between the Manifest variables
- Variable clustering based on non linear measures to discover the Factors
- Multiple clustering to create a hidden variable for each Factor
- Unsupervised learning with Manifest and Factor variables
One case study is showing a possible application of probabilistic structural equations :
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