Bayesian Statistics

Thu, Apr 29, 2021 One-minute read

Bayesian Statistics: Spring 2021

NOTE: Please understand that this course was given during the 2021 Covid Pandemic. An adherence to a normal course outline was not enforced and alot of manipulation of the original syllabus happened.

Outline

The course was structured into weekly concepts with overlap for exams.

  • Data and distributions
  • Frequentist versus Bayesian approach
  • What is Bayes's formula and what is a posterior
  • Sampling and sampling and sampling
  • The Metropolis algorithm and pymc3
  • Generalized Linear Models
  • Classifications and predictions
  • Switch points
  • Forcasting events

    • Structure

    The course content was sent out via video links and then supplemented with Zoom sessions. The first week was in-person and then the course moved remote. As such there is no videos for the first week.

    The students were give html versions of Jupyter Lab notebooks, primarily for students to get used to developing their own code. I will link both the html and videos associated with them

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