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2-Day Advanced BayesiaLab Course in Orlando, FL

Bayesia

Friday, October 25, 2013 at 9:00 AM - Saturday, October 26, 2013 at 5:00 PM (EDT)

Orlando, FL

Ticket Information

Ticket Type Sales End Price Fee Quantity
BayesiaLab Course (Commercial Tuition)   more info Ended $1,995.00 $0.00
BayesiaLab Course (Reduced Tuition for Gov't/Non-Profits)   more info Ended $1,495.00 $0.00
BayesiaLab Course (Reduced Tuition for Students/Faculty)   more info Ended $995.00 $0.00

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Event Details

Many BayesiaLab users have been asking for a course covering BayesiaLab's advanced features that are outside the scope of the 3-day introductory course. For the first time, and in conjunction with the BayesiaLab User Conference in Orlando, we are offering such an advanced program. The instructor, Dr. Lionel Jouffe, will be focusing on many of the recently introduced or enhanced features of BayesiaLab, such as knowledge elicitation and optimization. The advanced course is a 2-day program, scheduled to run from 9 to 5 on both days.

Course Topics

Course Content

    Manual Modeling with BEKEE
        Description of the workflow
        Exercise with the group of trainees

    Probability Table Analysis on Target State

    Parameter Sensitivity Analysis 
        Principle
        Exercise with a Fire Detection System

    Influence Diagrams
        Principle
        Example: Oil Rig
        Exercises: Monty Hall Problem, Investment Policy

    Dynamic Bayesian Networks
        Unfolded (Hidden Markov Chain example)
        Compact Form
            Temporal Simulation
            Exact and Approximate Inference
            Exercise with the Battery Example
        Network Temporalization
            Example with Box & Jenkins
            Forecast

    Bayesian Updating
        Principle
        Unfolded Form
        Compact Form
        Exercise with the Horse Doping Example

    Data Import
        Discretization
            Manual
            Automatic
            Rule of thumb for choosing the best method
        Aggregation
        Missing Values Processing
            Filtering
            Replacement
            Static Imputation
            Dynamic Imputation
            Structural EM
            Entropy Based Static Imputation
            Entropy Based Dynamic Imputation
        Filtered States
        Imputation
            Standard (law or Maximum a posteriori)
            Entropy based (law of MAP)
            Most Probable Explanation
        Exercise
            Creation of a network modeling MCAR, MAR and NMAR missing values,
            and Filtered States
            Data generation and learning

     Synthesis of New Variables
        Manual creation 
        Binarization
        KMeans and Bayesian Clustering on a subset of variables
        Binary Clustering
        Semi-Supervised Data Clustering
        Hierarchical Clustering
        Exercises

    Variable Clustering
        Cross-validation
        Comparison of solutions

    Fine Tuning of Learning
        Virtual Number of States
        Structural Coefficient
        Local Structural Coefficient
        Stratification
        Variable Selection with Cross-Validation
        Smooth Probability Estimation

    Analysis
        Evidence Analysis
        Likelihood Analysis
        Exercise for Mutli-Dimensional Outliers Detections
            Straw Models
            Analysis with the Less Probable Evidence
            Supervised Learning
        Maximum Probable Explanation
        Path Analysis
        Information Analysis

    Optimization
        Target Optimization

    Contributions
        Direct Effect Contributions
        Type I and Type II Contribution Analysis based on Counterfactuals
        Exercise on a Marketing Mix Example

    Negative and Disjunctive Inference
        Mapping
        Exercise

    Evidence Instantiation and Evidence Data Weighting
        Principle
        Exercise

    Design of Experiments
        Target
        Global

Terms & Conditions 

  • The tuition fee for the 2-day course for commercial participants is US$ 1,995.
  • Members of government agencies, the military and non-profit organizations are eligible for a reduced tuition fee of US$ 1,495 (25% discount).*
  • Student and faculty of accredited academic institutions are eligible for a reduced tuition fee of US$ 995 (50% discount).*
  • The course fee includes a Conference Pass for the BayesiaLab User Conference on October, 24, at the same venue as the course. 
  • A 60-day license to the full version of BayesiaLab Professional 5.1 will be provided to all participants for installation on their computers prior to the event.
  • Participants will be required to bring their own WiFi-enabled computer/laptop to the seminar (Windows XP/Vista/7/8 or Mac OS X).
  • The course fee includes all training materials, beverages, lunch and snacks during the training.
  • Accommodation in Orlando is at the participants' own expense, although negotiated rates are available at the seminar venue.

*Proof of affiliation will be required upon registration. If you are not sure about your eligibility for reduced tuition fees, please email us at info@bayesia.us.

Have questions about 2-Day Advanced BayesiaLab Course in Orlando, FL? Contact Bayesia

When & Where


Hilton Garden Inn Orlando at SeaWorld
6850 Westwood Blvd
Orlando, FL 32821

Friday, October 25, 2013 at 9:00 AM - Saturday, October 26, 2013 at 5:00 PM (EDT)


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Organizer

Bayesia

Bayesia USA is the North American sales and consulting organization for France-based Bayesia S.A.S. Their mission is to promote Bayesian networks as a new framework for knowledge discovery and reasoning within complex domains.

Founded by two professors in the field of artificial intelligence in 2001 and headquartered in northwestern France, Bayesia S.A.S. is the world's leading developer of research software based on the Bayesian network paradigm. Their principal product, BayesiaLab, is the only software platform that can perform unsupervised structural learning for knowledge discovery.

Today, BayesiaLab is being used by researchers in major organizations around the world, including P&G, Unilever, BBDO, GroupM, GfK, TNS, Ipsos, Mu Sigma, Booz Allen Hamilton, InterContinental Hotels Group, Dell and NASA's Jet Propulsion Laboratory among many others.

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