Abstract
In this chapter, we will briefly summarize the basic concepts of probability as well as graph theory.We start with important terms and definitions from graph theory and emphasize the relation to our hierarchical models. Directed and undirected graphs will be introduced and compared with each other. Then, we will give an overview of the random variables and the underlying probability distributions concentrating on nonparametric distributions and methods such as kernel density estimation. Finally, a simple hierarchical model is used to explain and illustrate inference methods.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Spehr, J. (2015). Probabilistic Graphical Models. In: On Hierarchical Models for Visual Recognition and Learning of Objects, Scenes, and Activities. Studies in Systems, Decision and Control, vol 11. Springer, Cham. https://doi.org/10.1007/978-3-319-11325-8_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-11325-8_2
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11324-1
Online ISBN: 978-3-319-11325-8
eBook Packages: EngineeringEngineering (R0)