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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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.
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