Introduction to Bayesian Inference
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
- 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.
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.
This course is a half-day introduction to Bayesian inference, and will take the form of a more traditional lecture format. Attendees are encouraged to ask questions throughout, and time is allotted for a more free-form question and answer session.
Prior to attending this course, participants should be familiar with basic concepts of probability and statistics, including common probability distributions and regression methods.