Bayes in the Milky Way: determining the dark matter profile in our galaxy, a novel...
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Antonio André Monteiro Manoel
Total Authors: 1
|Document type:||Doctoral Thesis|
|Institution:||Universidade de São Paulo (USP). Instituto de Física (IF/SBI)|
|Examining board members:|
Renato Vicente; Nestor Felipe Caticha Alfonso; Fabio Gagliardi Cozman; Jose Fernando Fontanari; Marcio Teixeira do Nascimento Varella
This thesis is divided into two parts. In the first part, we show how problems of statistical inference and combinatorial optimization may be approached within a unified framework that employs tools from fields as diverse as machine learning, statistical physics and information theory, allowing us to i) design algorithms to solve the problems, ii) analyze the performance of these algorithms both empirically and analytically, and iii) to compare the results obtained with the optimal achievable ones. In the second part, we use this framework to study two specific problems, one of inference (compressed sensing) and the other of optimization (information hiding). In both cases, we review current approaches, identify their flaws, and propose new schemes to address these flaws, building on the use of message-passing algorithms, variational inference techniques, and spin glass models from statistical physics. (AU)
|FAPESP's process:||12/12363-8 - Statistical Mechanics of Steganographic Systems|
|Grantee:||Antonio Andre Monteiro Manoel|
|Support Opportunities:||Scholarships in Brazil - Doctorate|