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- Production optimisation through feedback analysis
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- Football World Cup 2010
- Health trajectory analysis
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- 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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Analysis of a perfume market
This presentation is mainly based on the slides used for AppliBUGS 2009. We first introduce Bayesian Belief Networks, and then, we describe how Probabilistic Structural Equations (PSE) can be induced by BayesiaLab to analyze a specific Perfume Market. We also have updated the AppliBUGS presentation in order to describe the Mutli-Quadrant Analysis. Indeed, this new analysis tool allows taking into account the competitive position of each product's drivers for the computation of the optimal policies.
For a detailed BayesiaLab's tutorial describing the complete workflow to get Probabilistic Structural Equations, the interested reader is referred to "Probabilistic Structural Equations and Path Analysis"
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