Français Search
www.bayesia.com does not fully support your browser (Internet Explorer 6).
We suggest upgrading to IE 7 or downloading Firefox for a more enjoyable web experience.

Resources

BayesiaLab in action: presentations and case studies

LinkedIn  Join our discussion group "Bayesian Belief Networks with BayesiaLab".

TutorialSoftware presentations

  • Introduction to Bayesian networks Discover the world of Bayesian networks.

    Read articleRead  Download articleDownload (7.1 MB)

  • Bayesialab's introductory presentation Discover the world of the Bayesian networks with this first presentation of BayesiaLab.

    Watch : Introductory presentationWatch (5 min.)  Download articleDownload (1.4 MB)

  • Analysis toolbox Bayesian networks are naturally legibles. BayesiaLab goes further with its very complete analysis toolbox.

    Watch : AnalysisWatch (7 min.)  Download articleDownload (1.9 MB)

  • Decision support Use of Decision and Utility nodes to discover the optimal action policies on a simple example: drilling of oil wells.

    Download articleDownload (355 kB)

  • Bayesian network learning BayesiaLab is a powerful data mining tool that allows easily and simply discovering the knowledge hidden in your databases.

    Watch : LearningWatch (10 min.)  Download articleDownload (1.8 MB)

  • 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.

    Watch : Dynamic networksWatch (9 min.)  Download articleDownload (1.9 MB)

  • 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

    Download articleDownload (1.1 MB)

  • 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.

    Read articleRead  Download articleDownload (7.5 MB)

  • 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.

    Read articleRead  Download articleDownload (6.1 MB)

  • 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.

    Read articleRead  Download articleDownload (10.1 MB)

  • 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.

    Read articleRead  Download articleDownload (3.7 MB)

  • 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.

    Read articleRead  Download articleDownload (8.8 MB)

  • 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.

    Read articleRead  Download articleDownload (4.5 MB)

  • 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.

    Read articleRead  Download articleDownload (3.7 MB)

  • 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.

    Read articleRead  Download articleDownload (8.8 MB)

  • Missing Values Imputation Nouveau 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.

    Read articleRead  Download articleDownload (26.5 MB)

User guide BayesiaLab User guide

User guide BayesiaLab Technical Specifications

Case studiesCase studies

Industry

Marketing

Health

  • Microarray analysis with Bayesian Networks In this study, we turn to the field of cancer classification by means of microarray analysis. Microarray analysis is a technique for gene expression profiling of cell samples. Expression profiles indicate which genes are currently active among thousands of genes.

    Read articleRead  Download articleDownload (5.4 MB)

  • Breast Cancer Diagnostics with Bayesian Networks Our white paper reevaluates the Wisconsin Breast Cancer Database within the framework of Bayesian networks, which, to our knowledge, has not been done before.

    Read articleRead  Download articleDownload (5.1 MB)

  • Difficult intubations analysis Presentation in conferenceScata 2007 (London) Prediction of difficult intubation (DI) is crucial during pre anaesthesia assessment of a patient. Many criterions are used to predict DI with different performances. In this case study, we show how BayesiaLab, through its learning algorithms, allows to quickly discovering unknown probabilistic relationships between variables and enhance prediction.

    Download articleDownload (722 kB)

  • Salmonella isolation Presentation in conference20th International Pig Veterinary Society Congress, 2008, Durban (South Africa) Identification of factors associated with Salmonella isolation on pork carcasses via bayesian networks.

    Download articleDownload (240 kB)

  • Biocomputing transcriptome analysis Bioinformatics with BayesiaLab.

    Read articleRead 

  • Health trajectory analysis Prediction of medical needs with BayesiaLab.

    Read articleRead 

Strategy

Risk management

Others