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Document Type : Latin Dissertation
Language of Document : English
Record Number : 150525
Doc. No : ET22317
Main Entry : Aaron Angelo D'Souza
Title Proper : TOWARDS TRACTABLE PARAMETER-FREE STATISTICAL LEARNING
Note : This document is digital این مدرک بصورت الکترونیکی می باشد
Abstract : The objectivity of statistical analysis hinges on the assumptions made about the form andcomplexity of the model used to fit the data. These usually take the guise of "nuisanceparameters" which must be set based on some meta-level knowledge of the problem tobe solved. This dissertation seeks to contribute statistical methods which require as littlemeta-level knowledge as possible, and yet are computationally and analytically tractableenough to operate on real-world datasets.This goal is partially achieved within the framework of Bayesian statistics, whichallows the specification of prior knowledge, and-...,..tested for theQ1 PC1 bus cardBoth these projects mere sofixare des elopment efforts tonards contributing to dlfferentaspects of Roboucs and lZ1echatronics projects m the Controls and Roboucs Group..
Subject : Electericl tess
: برق
electronic file name : TL45523.pdf
Title and statement of responsibility and : TOWARDS TRACTABLE PARAMETER-FREE STATISTICAL LEARNING [Thesis]
 
 
 
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