Skip to Main content Skip to Navigation
Book sections

Modelling structured data with probabilistic graphical models

Florence Forbes 1
1 MISTIS - Modelling and Inference of Complex and Structured Stochastic Systems
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
Abstract : Most clustering and classification methods are based on the assumption that the objects to be clustered are independent. However, in more and more modern applications, data are structured in a way that makes this assumption not realistic and potentially misleading. A typical example that can be viewed as a clustering task is image segmentation where the objects are the pixels on a regular grid and depend on neighbouring pixels on this grid. Also, when data are geographically located, it is of interest to cluster data with an underlying dependence structure accounting for some spatial localisation. These spatial interactions can be naturally encoded via a graph not necessarily regular as a grid. Data sets can then be modelled via Markov random fields and mixture models (e.g. the so-called MRF and Hidden MRF). More generally, probabilistic graphical models are tools that can be used to represent and manipulate data in a structured way while mod-eling uncertainty. This chapter introduces the basic concepts. The two main classes of probabilistic graphical models are considered: Bayesian networks and Markov networks. The key concept of conditional independence and its link to Markov properties is presented. The main problems that can be solved with such tools are described. Some illustrations are given associated with some practical work.
Complete list of metadatas

https://hal.archives-ouvertes.fr/hal-01423613
Contributor : Florence Forbes <>
Submitted on : Tuesday, January 3, 2017 - 3:41:58 PM
Last modification on : Thursday, March 26, 2020 - 8:49:32 PM
Document(s) archivé(s) le : Tuesday, April 4, 2017 - 12:30:42 PM

File

MyChap4HAL.pdf
Files produced by the author(s)

Identifiers

Collections

Citation

Florence Forbes. Modelling structured data with probabilistic graphical models. D. Fraix-Burnet; S. Girard. Statistics for Astrophysics- Classification and Clustering, 77, EDP Sciences, pp.195-219, 2016, EAS Publication Series, ⟨10.1051/eas/1677009⟩. ⟨hal-01423613⟩

Share

Metrics

Record views

801

Files downloads

1626