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.
Sorry, there are no upcoming events for this course, but please get in touch if you would like to be kept informed when events are scheduled in the future.View our full training course calendar »
By the end of this course participants will…
Prior to attending this course, participants should be familiar with basic concepts of probability and statistics, including common probability distributions and regression methods.