Learning minimal latent directed information trees

Jalal Etesami; Negar Kiyavash; Todd P. Coleman

doi:10.1109/ISIT.2012.6284017

Learning minimal latent directed information trees

Jalal Etesami
, Negar Kiyavash
, Todd P. Coleman

Industrial and Enterprise Systems Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

THIS PAPER IS ELIGIBLE FOR THE STUDENT PAPER AWARD- We propose a framework for learning the structure of a minimal latent tree with an associated discrepancy measure. Specifically, we apply this algorithm to recover the minimal latent directed information tree on a mixture of set of observed and unobserved random processes. Directed information trees are a new type of probabilistic graphical model based on directed information that represent the casual dynamics among random processes in a stochastic systems. To the best of our knowledge, this is the first approach that recovers these type of latent graphical models where samples are available only from a subset of processes.

Original language	English (US)
Title of host publication	2012 IEEE International Symposium on Information Theory Proceedings, ISIT 2012
Pages	2726-2730
Number of pages	5
DOIs	https://doi.org/10.1109/ISIT.2012.6284017
State	Published - 2012
Event	2012 IEEE International Symposium on Information Theory, ISIT 2012 - Cambridge, MA, United States Duration: Jul 1 2012 → Jul 6 2012

Publication series

Name	IEEE International Symposium on Information Theory - Proceedings

Other

Other	2012 IEEE International Symposium on Information Theory, ISIT 2012
Country/Territory	United States
City	Cambridge, MA
Period	7/1/12 → 7/6/12

ASJC Scopus subject areas

Theoretical Computer Science
Information Systems
Modeling and Simulation
Applied Mathematics

Online availability

10.1109/ISIT.2012.6284017

Library availability

Discover UIUC Full Text

Cite this

Etesami, J, Kiyavash, N & Coleman, TP 2012, Learning minimal latent directed information trees. in 2012 IEEE International Symposium on Information Theory Proceedings, ISIT 2012., 6284017, IEEE International Symposium on Information Theory - Proceedings, pp. 2726-2730, 2012 IEEE International Symposium on Information Theory, ISIT 2012, Cambridge, MA, United States, 7/1/12. https://doi.org/10.1109/ISIT.2012.6284017

@inproceedings{fceafa2976f14ad4a71771d68e1d4fb4,

title = "Learning minimal latent directed information trees",

abstract = "THIS PAPER IS ELIGIBLE FOR THE STUDENT PAPER AWARD- We propose a framework for learning the structure of a minimal latent tree with an associated discrepancy measure. Specifically, we apply this algorithm to recover the minimal latent directed information tree on a mixture of set of observed and unobserved random processes. Directed information trees are a new type of probabilistic graphical model based on directed information that represent the casual dynamics among random processes in a stochastic systems. To the best of our knowledge, this is the first approach that recovers these type of latent graphical models where samples are available only from a subset of processes.",

author = "Jalal Etesami and Negar Kiyavash and Coleman, \{Todd P.\}",

year = "2012",

doi = "10.1109/ISIT.2012.6284017",

language = "English (US)",

isbn = "9781467325790",

series = "IEEE International Symposium on Information Theory - Proceedings",

pages = "2726--2730",

booktitle = "2012 IEEE International Symposium on Information Theory Proceedings, ISIT 2012",

note = "2012 IEEE International Symposium on Information Theory, ISIT 2012 ; Conference date: 01-07-2012 Through 06-07-2012",

}

TY - GEN

T1 - Learning minimal latent directed information trees

AU - Etesami, Jalal

AU - Kiyavash, Negar

AU - Coleman, Todd P.

PY - 2012

Y1 - 2012

N2 - THIS PAPER IS ELIGIBLE FOR THE STUDENT PAPER AWARD- We propose a framework for learning the structure of a minimal latent tree with an associated discrepancy measure. Specifically, we apply this algorithm to recover the minimal latent directed information tree on a mixture of set of observed and unobserved random processes. Directed information trees are a new type of probabilistic graphical model based on directed information that represent the casual dynamics among random processes in a stochastic systems. To the best of our knowledge, this is the first approach that recovers these type of latent graphical models where samples are available only from a subset of processes.

AB - THIS PAPER IS ELIGIBLE FOR THE STUDENT PAPER AWARD- We propose a framework for learning the structure of a minimal latent tree with an associated discrepancy measure. Specifically, we apply this algorithm to recover the minimal latent directed information tree on a mixture of set of observed and unobserved random processes. Directed information trees are a new type of probabilistic graphical model based on directed information that represent the casual dynamics among random processes in a stochastic systems. To the best of our knowledge, this is the first approach that recovers these type of latent graphical models where samples are available only from a subset of processes.

UR - https://www.scopus.com/pages/publications/84867513371

UR - https://www.scopus.com/pages/publications/84867513371#tab=citedBy

U2 - 10.1109/ISIT.2012.6284017

DO - 10.1109/ISIT.2012.6284017

M3 - Conference contribution

AN - SCOPUS:84867513371

SN - 9781467325790

T3 - IEEE International Symposium on Information Theory - Proceedings

SP - 2726

EP - 2730

BT - 2012 IEEE International Symposium on Information Theory Proceedings, ISIT 2012

T2 - 2012 IEEE International Symposium on Information Theory, ISIT 2012

Y2 - 1 July 2012 through 6 July 2012

ER -

Learning minimal latent directed information trees

Abstract

Publication series

Other

ASJC Scopus subject areas

Online availability

Library availability

Related links

Fingerprint

Cite this