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.