Training Course Details

Introduction to Bayesian Inference

Introduction to Bayesian Inference

Course Level: Foundation

The capturing and quantification of uncertainty is a very important aspect of model-fitting and parameter inference. Bayesian inference represents a fully-probabilistic approach to parameter inference, allowing a practitioner to quantify their uncertainties through probability densities. However, fitting models in a Bayesian framework can be an involved and complicated affair, often necessitating the use of Markov chain Monte Carlo (MCMC) algorithms and their programmatic implementation.

Online | February 1, 2021,

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Course Details

  • Course Outline
  • Learning Outcomes
  • Materials
  • Prior Knowledge

Course Outline

  • Bayesian inference: Motivation of the Bayesian philosophy and an introduction to Bayes’ Theorem.
  • Markov chain Monte Carlo (MCMC) methods: An overview of MCMC methods and the problems they seek to overcome.
  • Posterior predictive simulation: Capturing uncertainty about the predictions of a model.

Learning Outcomes

By the end of this course participants will…

  • Understand the merits of the Bayesian workflow and the importance of uncertainty quantification.
  • Have developed an intuitive understanding of prior beliefs, the likelihood function, and the posterior distribution.
  • Be able to articulate the difficulties of fitting models in a Bayesian framework.
  • Understand how MCMC algorithms work and how they can be used to alleviate difficulties in model-fitting.


Prior Knowledge

Prior to attending this course, participants should be familiar with basic concepts of probability and statistics, including common probability distributions and regression methods.

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