A graphical model or probabilistic graphical model (PGM) or structured probabilistic model is a probabilistic model for which a graph expresses the conditional dependence structure between random variables.

Probabilistic graphical models provide a modular representation for complex joint probability distributions. Nodes can represent pixels, scenes, objects, or object parts, while the edges describe the conditional independence structure.

We will consider these two ways in some detail: We could find a function which can directly map an input value to it’s class label. We can find the probability distributions over the variables and then use this distribution to answer queries about the new data point. There are a lot of algorithms for finding a mapping function.

We review three rep-resentations of probabilistic graphical models, namely, Markov networks or undirected graphical models, Bayesian networks or directed graphical models, and factor graphs. Then, we provide an overview about structure and parameter learning techniques.

Part of the book series: Advances in Computer Vision and Pattern Recognition (ACVPR) This fully updated new edition of a uniquely accessible textbook/reference provides a general introduction to probabilistic graphical models (PGMs) from an engineering perspective.

Jul 15, 2020 · Probabilistic graphical model (PGM) provides a graphical representation to understand the complex relationship between a set of random variables (RVs). RVs represent the nodes and the statistical dependency between them is called an edge.

Graphical models provide a powerful and intuitive framework for modelling and inference. Directed, undirected and factor graphs. Inference by message passing. Parameter and structure learning.

Several possible ways to acquire a model: Use expert knowledge to determine the graph and the potentials. Use data+learning to determine the potentials, i.e., parameter learning. Use data+learning to determine the graph, i.e., structure learning.

Probabilistic Graphical Models: Principles and Techniques / Daphne Koller and Nir Friedman. p. cm. – (Adaptive computation and machine learning) Includes bibliographical references and index.

•One of the powerful aspects of graphical models is that a specific graph can make probabilistic statements for a broad class of distributions •We say this graph is fully connected ifthere is a link between every pair of nodes •Consider an arbitrary joint distribution •One may apply the product rule of probability pp(a;b;c)