Bayesian parameter estimation in exponential families
(30 minutes to learn)
Exponential families are convenient for Bayesian parameter estimation because the conjugate priors often have a convenient form, and there is a simple form for the posterior.
This concept has the prerequisites:
- How do you derive the conjugate prior for an exponential family distribution?
- Show that the posterior can be computed in terms of the sufficient statistics.
- Work through a simple example, such as the beta-Bernoulli model.
Core resources (read/watch one of the following)
→ Machine Learning: a Probabilistic Perspective
A very comprehensive graudate-level machine learning textbook.
Location: Section 9.2.5, "Bayes for the exponential family," pages 287-289
-No Additional Notes-
- create concept: shift + click on graph
- change concept title: shift + click on existing concept
- link together concepts: shift + click drag from one concept to another
- remove concept from graph: click on concept then press delete/backspace
- add associated content to concept: click the small circle that appears on the node when hovering over it
- other actions: use the icons in the upper right corner to optimize the graph placement, preview the graph, or download a json representation