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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Resources
BayesiaLab in action: software presentations
- Introduction to Bayesian networks Discover the world of Bayesian networks.
- Bayesialab's introductory presentation Discover the world of the Bayesian networks with this first presentation of BayesiaLab.
- Analysis toolbox Bayesian networks are naturally legibles. BayesiaLab goes further with its very complete analysis toolbox.
- Decision support Use of Decision and Utility nodes to discover the optimal action policies on a simple example: drilling of oil wells.
- Bayesian network learning BayesiaLab is a powerful data mining tool that allows easily and simply discovering the knowledge hidden in your databases.
- 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.
- Filtered/Censored States In a lot of studies, one needs to use variables that are contingent on context, i.e. variables that only exist depending on the values of other variables (usage, configuration). We describe here how BayesiaLab takes rigorously these variables into account during learning and analysis
- Driver analysis and product optimization with BayesiaLab This tutorial covers Driver Analysis and Product Optimization with the Probabilistic Structural Equation Models of BayesiaLab. It provides hands-on examples of how to use Bayesian networks in the field of marketing science.
- Causal Inference and Direct Effects The format of this document is essentially “two papers in one,” with the first chapter focusing on mostly theoretical considerations (although illustrated with an example), while the second chapter provides a practical, real-world example presented in the form of a tutorial.
- Unsupervised and Supervised Learning with BayesiaLab Perhaps more than any other kind of time series data, financial markets have been scrutinized by countless mathematicians, economists, investors and speculators over hundreds of years. Even in modern times, despite all scientific advances, the effort of predicting future movements of the stock market sometimes still bears resemblance to the ancient alchemistic aspirations of turning base metals into gold. That is not to say that there is no genuine scientific effort in studying financial markets, but distinguishing serious research from charlatanism (or even fraud) remains remarkably difficult.
- Paradoxes and Fallacies There are a number of paradoxes and fallacies that keep recurring as popular and mind-bending puzzles in the media. Although there is (now) complete agreement among scientists on how to resolve them, the correct answers are often perplexing to the casual observer and still cause bewilderment. As this paper will show, the formally correct solutions of these probabilistic paradoxes are counterintuitive.
- Probabilistic Latent Factor Induction and Statistical Factor Analysis It is not surprising that the new Bayesian network paradigm prompts comparisons to more conventional methods. In the field of market research, for instance, long-established methods, such as factor analysis remain in daily use today. Given that there exists a direct counterpart to factor analysis in the Bayesian network framework, we want to highlight similarities as well as fundamental differences. The objective of this paper is to present both methods side-by-side and thus help researchers to correctly compare and interpret the respective results. More specifically, we want to establish the semantic equivalents between the traditional statistical factor analysis approach and BayesiaLab’s method based on Bayesian networks, which we refer to as Probabilistic Latent Factor Induction.
- 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.
- BayesiaLab Knowledge Elicitation Environment This presentation describes the new BayesiaLab 5.0 Knowledge Elicitation Environment. This environment allows reducing biases (cognitive, group and facilitator), and allows to greatly improve the traceability of the brainstorming session.
- Modeling Vehicle Choice and Simulating Market Share with Bayesian Networks We present a new method and the associated workflow for estimating market shares of future products based exclusively on pre-introduction data, such as syndicated studies conducted prior to product launch.
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Missing Values Imputation
As missing values processing (beyond the naïve ad-hoc approaches) can be a demanding task, both methodologically and computationally, the principal objective of this paper is to propose a new and hopefully easier approach by employing Bayesian networks. It is not our intention to open the proverbial “new can of worms”, and thus distract researchers from their principal study focus, but rather we want to demonstrate that Bayesian networks can reliably, efficiently and intuitively integrate missing values processing into the main research task.
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