Bayesian Hierarchical Modeling

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  • Learn how to fit and compare ecological models in a Bayesian hierarchical framework, which separates the observation process from the ecological process when estimating spatial and/or temporal variation in parameters such as abundance, density, and survival.

  • TOPICS

    • What is a hierarchical model?

    • Random effects and hyperparameters

    • Separating the observation process from the ecological process

    • Observation-process models for data from studies involving mark-recapture, distance sampling, occupancy, sightings of unmarked individuals, and citizen-science

    • What is a state-space model?

    • Spatial modelling of abundance/density, occupancy, and survival

    • Spatio-temporal modelling of abundance/density, occupancy, and survival

    • Allowing for spatial and/or temporal autocorrelation

    • Use of spatial and/or temporal covariates

    • Choice of priors

    • Checking model-fit

    • Comparing two or more models

    LEARNING OUTCOMES

    • Understand how to separate the observation-process from the ecological-process

    • Understand which type of observation-process model is appropriate for your data

    • Gain experience fitting spatio-temporal models to your data in R

    • Understand how to check the fit of a spatio-temporal model in R

    • Be aware of the potential need to allow for spatial and/or temporal autocorrelation

    • Know how to compare the predictive ability of different spatio-temporal models

COURSE OPTIONS & INFORMATION (Review chart above, then click below)

  • FORMAT:

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

    CONTINUING EDUCATION:

    DATES & PRICES:

    • Winter: Dec 2 - Feb 23 (Early bird ends Nov 4th): $275 student / $375 professional

    *Early bird saves $75

  • 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: Dec 2 - Feb 23 (Early bird ends Nov 4th): $425 student / $525 professional

    *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: Dec 2 - Feb 23 (Early bird ends Nov 4th): $575 professional / $675 student

    *Early bird saves $75

INSTRUCTOR

TESTIMONIALS

“David Fletcher's Bayesian Hierarchical Modeling course is excellent. The lectures, examples, and exercises are based on real ecological data collected from a variety of approaches, including mark-recapture, distance sampling, community science, and more. There is good (but not overwhelming) discussion of the mathematics underlying the models and emphasis on model fit, refinement, and selection. I was quickly able to apply the techniques I learned in this course to my own messy dataset, which helped get me past some of the challenges I was facing using a frequentist approach.

Joshua Morris

Urban Conservation Manager at Birds Connect Seattle

 

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.