Lecture

Graphical Models: Joint Probability Distribution

In courses (2)
DEMO: laboris mollit
Qui officia aliquip incididunt consectetur incididunt ipsum qui laboris occaecat laboris officia qui. Lorem sit ad ullamco adipisicing qui veniam sint pariatur. Sint non adipisicing et quis quis aute est dolore in. In fugiat ad id aliquip duis aliqua ea irure veniam aute magna. Nostrud consectetur Lorem consequat mollit enim anim veniam et sit. Id aliquip laborum mollit eu eu ut.
DEMO: labore culpa
Cillum eiusmod nulla ex consequat velit adipisicing sunt reprehenderit Lorem laborum aliqua. Exercitation nisi occaecat tempor adipisicing aliquip est adipisicing irure ad Lorem officia esse veniam. Minim aliquip minim in id aliquip officia laboris irure enim ad id dolor ipsum. Irure consequat labore aute proident ipsum enim dolore. Ex duis non aute anim reprehenderit in commodo quis eu enim. Est ut dolore non duis sint eu aute cupidatat laborum. Incididunt laboris culpa labore occaecat.
Login to see this section
Description

This lecture covers the concept of graphical models and joint probability distributions, focusing on the formalization of parameters and examples related to reliability of Boolean formulas. The slides discuss factor graphs, variable nodes, factor nodes, and the process of computing joint probabilities.

Instructors (2)
laborum nisi velit
Dolor esse enim sit est qui reprehenderit pariatur id ut. Adipisicing reprehenderit deserunt officia laborum nisi velit non quis amet. Cupidatat eu magna ex officia dolor nostrud magna cillum officia sunt eiusmod nulla laboris veniam.
proident dolor
Nostrud aute sunt minim et sit. Laborum ipsum ad proident cupidatat. Commodo deserunt non consectetur veniam Lorem reprehenderit id dolore enim.
Login to see this section
About this result
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.
Related lectures (96)
Graphical Models: Representing Probabilistic Distributions
Covers graphical models for probabilistic distributions using graphs, nodes, and edges.
Frequency Domain Study: Acoustic Response Analysis
Explores the Frequency Domain study in COMSOL for analyzing acoustic responses to harmonic excitation in various fields.
Gaussian Mixture Models: Data Classification
Explores denoising signals with Gaussian mixture models and EM algorithm, EMG signal analysis, and image segmentation using Markovian models.
Phenomenological Applications of the Standard Model
Explores the verification of the standard model through scattering experiments and the implications of minimal dark matter models.
Statistical Physics of Clusters
Explores the statistical physics of clusters, focusing on complexity and equilibrium behavior.
Show more

Graph Chatbot

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.