Resources
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Tutorials
- Introduction to Bayesian networks
- Bayesialab's introductory presentation
- Analysis toolbox
- Bayesian network learning
- Decision support
- Dynamic bayesian networks and action policy learning
- Filtered/Censored States
- Probabilistic structural equations
- BayesiaLab Knowledge Elicitation Environment
- Driver analysis and product optimization with BayesiaLab
- Modeling Vehicle Choice and Simulating Market Share
- Probabilistic Latent Factor Induction
- Paradoxes and Fallacies
- Unsupervised and Supervised Learning with BayesiaLab
- Causal Inference and Direct Effects
- Causal Inference and Direct Effects
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User Guide
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BayesiaLab Technical Specifications
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Case studies
- Production optimisation through feedback analysis
- Satisfaction questionnaire analysis
- Football World Cup 2010
- Breast Cancer Diagnostics
- Microarray 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
- Driver Analysis: The Missing Links
- 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
Here are other applications of our Probabilistic Structural Equations models :
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