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en:projects:details:epc09 [2009/06/05 18:58]
masoud
en:projects:details:epc09 [2016/06/23 11:26] (current)
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-===== Expectation Propagation Decoding ===== + ---- dataentry project ----
-\\ +
-  +
-**Expectation Propagation** is a method for approximate inference in Graphical Models. The aim of this project is to treat the tanner graph of LDPC code as a graphical model and to use EP technique for decoding. The proportion of theoretical and empirical (programming,​ simulation) work in this project is 40-60 percent. +
- +
-Requirement:​ +
-  * Good familiarity with basic probability theory +
-  * Good programming skills in Matlab and/or C/C++ +
-What will you learn +
-  * LDPC codes +
-  * Learning and Inference in Graphical Models +
-  * Expectation Propagation +
-  * Fast implementation of approximate inference in graphical models +
-Suggested Readings and References  +
-  * [[http://​research.microsoft.com/​en-us/​um/​people/​minka/​papers/​ep/​|Thomas Minka'​s EP page]] +
-  * [[http://​videolectures.net/​abi07_walsh_cip/​|A Completed Information Projection Interpretation of Expectation Propagation]] +
- +
----- dataentry project ----+
 title :  Expectation Propagation Decoding ​   title :  Expectation Propagation Decoding ​  
 contactname:​ Masoud Alipour contactname:​ Masoud Alipour
-contactmail_mail:​ masoud ​[dot] alipour ​[at] epfl [dot] ch+contactmail_mail:​ masoud.alipour@epfl.ch
 contacttel: 021 6937529 contacttel: 021 6937529
 contactroom:​ BC 150 contactroom:​ BC 150
 type : master semester ​ type : master semester ​
-status ​available+state unavailable
 created_dt : 2009-06-03 created_dt : 2009-06-03
 taken_dt :  ​ taken_dt :  ​
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 template:​datatemplates:​project template:​datatemplates:​project
 ---- ----
 +
 +**Expectation Propagation** is a method for approximate inference in Graphical Models. The aim of this project is to treat the tanner graph of LDPC code as a graphical model and to use EP technique for decoding. The proportion of theoretical and empirical (programming,​ simulation) work in this project is 40-60 percent.
 +
 +Requirement:​
 +  * Good familiarity with basic probability theory
 +  * Good programming skills in Matlab and/or C/C++
 +What will you learn
 +  * LDPC codes
 +  * Learning and Inference in Graphical Models
 +  * Expectation Propagation
 +  * Fast implementation of approximate inference in graphical models
 +Suggested Readings and References ​
 +  * [[http://​research.microsoft.com/​en-us/​um/​people/​minka/​papers/​ep/​|Thomas Minka'​s EP page]]
 +  * [[http://​videolectures.net/​abi07_walsh_cip/​|A Completed Information Projection Interpretation of Expectation Propagation]]
 +