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Tutorials
- Introduction to Bayesian networks
- Bayesialab's introductory presentation
- Analysis toolbox
- Bayesian network learning
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- Dynamic bayesian networks and action policy learning
- Filtered/Censored States
- Probabilistic structural equations
- 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
- BEKEE, Bayesia Expert Knowledge Elicitation Environment
- 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
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- 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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Driver analysis and product optimization with BayesiaLab
This tutorial covers Driver Analysis and Product Optimization with BayesiaLab. It provides hands-on examples of how to use Bayesian networks in the field of marketing science.
In this study we want to examine how product attributes perceived by consumers relate to purchase intention for specific products. Put simply, we want to understand the key drivers for purchase intent. Given the large number of attributes in our study, we also want to identify common concepts among these attributes in order to make interpretation easier and communication with managerial decision makers more effective.
Secondly, we want to utilize the generated understanding of consumer dynamics, so product developers can optimize the characteristics of the products under study in order to increase purchase intent among consumers, which is our ultimate business objective.
Here are other applications of our Probabilistic Structural Equations models :
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