Bayesian Causal Network Modeling

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  • This course is designed to provide the fundamental understanding and setting up of Bayesian Causal Network (BCN) models for integrative analyses. The course will provide several aspects of comprehension for Bayesian network modeling including: a) some background on bayesian modeling and its uses, b) practice based on individual interests in setting up a basic research plan for collecting appropriate data and c) using that data (or practice data for the standard format) to set up a basic network. Along the way we will look into troubleshooting common problems, how to streamline the creation of the CPT, and how to assess ways to maximize strategic outcomes.

    • No prior experience with Bayesian statistics is expected.

    • Various publications, webpages, and other resources introduced throughout the course

    • Recorded and print lecture materials

    • Tutorial videos and written materials

  • MAIN COURSE OBJECTIVE

    To become proficient in using Netica to conduct Bayesian Causal Network (BCN) operations, model strategic outcomes, and update BCN models so that models can be used for current and future analysis.

    Module 1: Introduction to Bayesian causal network (BCN) modeling concepts

    • Objective: To understand the basis for Bayesian networks as well as their applications and to lay the foundation for model development.

      Outcomes:

      ▪ Develop a foundation for applying Bayesian networks

      ▪ Researching uses and frameworks for BCN to guide your design

      ▪ Discussion of research topics you will be considering for BCN use

      ▪ Annotated bibliography

    Module 2: BCN preparation and Decision Nets

    • Objective: To lay the structural foundation for a Bayesian network.

      Outcomes:

      ▪ Install and load Netica®

      ▪ Create an influence diagram

      ▪ Create a Decision Net

    Module 3: Setting up a basic Bayesian network

    • Objective: Developing proficiency in creating a functional Bayesian network.

      Outcomes:

      ▪ Creating a Bayesian Causal Network

      ▪ Simplifying the network

      ▪ Calibrating the network

      ▪ Sensitivity analysis

    Module 4: Expanding and modifying your Bayesian network

    • ▪ Aggregating to the model and updating with new information

      ▪ Sensitivity analysis

      ▪ Scenario testing

      ▪ Final model acts as the final exam

    Module 5: (optional) Applied project creating your own applied Bayesian network

COURSE FORMATS, DATES, & PRICING (See Chart above)

  • FORMAT:

    • 3 months of access to course materials as you work at your own pace 

    • Get instructor support for the 3-month term via email, discussion threads, group meetings, and one-on-one appointments

    • After working through the course materials, set up an optional meeting with the instructor to discuss your own personal project from work or school

    CONTINUING EDUCATION:

    CERTIFICATIONS:

    DATES & PRICES:

    • Winter : Jan 6 - Mar 23: $425 student / $525 professional (early bird* ends Dec 2nd)

    *Early bird saves $75

  • FORMAT:

    • 12 months of access to course materials as you work at your own pace 

    • Get instructor support for the 3-month term via email, discussion threads, group meetings, and one-on-one appointments

    • After working through the course materials, set up an optional meeting with the instructor to discuss your own personal project from work or school

    CONTINUING EDUCATION:

    CERTIFICATIONS:

    DATES & PRICES:

    • Winter : Jan 6 - Mar 23: $575 student / $675 professional (early bird* ends Dec 2nd)

    *Early bird saves $75

Instructor

Dr. Mira Mishkin

Environmental Geographer

 
 

SCHOLARSHIPS

Full scholarships are available to participants from countries designated as “lower income” and “lower middle income” in the World Bank List of Economies. Please see our CWS World Scholars Program page for details.

CANCELLATION POLICY

Cancellations 30 days or more before the start date are not subject to cancellation fees. Cancellations <30 days before the start date are subject to a 50% cancellation fee. No refunds once the course begins.