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	<title>eng.kulanov.org.ua &#187; Statistics</title>
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	<link>http://eng.kulanov.org.ua</link>
	<description>GRID Compiting, Grid in Ukraine, Workload performance, modelling and prediction</description>
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		<title>MLE, MAP, Bayes</title>
		<link>http://eng.kulanov.org.ua/archives/121</link>
		<comments>http://eng.kulanov.org.ua/archives/121#comments</comments>
		<pubDate>Mon, 20 Sep 2010 13:56:33 +0000</pubDate>
		<dc:creator>Sergey Kulanov</dc:creator>
				<category><![CDATA[Statistics]]></category>
		<category><![CDATA[bayes]]></category>
		<category><![CDATA[map]]></category>
		<category><![CDATA[mle]]></category>

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		<description><![CDATA[Estimation: ML, Bayes, MAP Principles and calculus at http://www-users.cselabs.umn.edu/classes/Fall-2008/csci5525/notes/estimate.pdf Estimation Simple explanation here: Given the evidence X, MLE considers the parameter vector Θ to be a constant and seeks out that value for the constant that provides maximum support for the evidence. ML does NOT allow us to inject our prior beliefs about the likely [...]]]></description>
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		<title>Viterbi algorithm Demo</title>
		<link>http://eng.kulanov.org.ua/archives/116</link>
		<comments>http://eng.kulanov.org.ua/archives/116#comments</comments>
		<pubDate>Sat, 11 Sep 2010 06:45:22 +0000</pubDate>
		<dc:creator>Sergey Kulanov</dc:creator>
				<category><![CDATA[Matlab]]></category>
		<category><![CDATA[Statistics]]></category>
		<category><![CDATA[HMM]]></category>
		<category><![CDATA[Viterbi]]></category>

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		<description><![CDATA[En excellent tutorial on Viterbi Algorithm was fount at http://www.telecom.tuc.gr/~ntsourak/demo_viterbi.htm]]></description>
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		<title>Covariance Matrix</title>
		<link>http://eng.kulanov.org.ua/archives/88</link>
		<comments>http://eng.kulanov.org.ua/archives/88#comments</comments>
		<pubDate>Thu, 06 May 2010 07:25:01 +0000</pubDate>
		<dc:creator>Sergey Kulanov</dc:creator>
				<category><![CDATA[Matlab]]></category>
		<category><![CDATA[Statistics]]></category>

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		<description><![CDATA[Good description and simple explanation is given here Covariance matrix The variance of a variable is a measure of the dispersion of the values taken by the variable around its mean value. The covariance matrix generalizes the concept of variance to random vectors, or sets of random variables. PCA and covariance matrix both are well explained. [...]]]></description>
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		<slash:comments>1</slash:comments>
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		<title>Bayes’ Rule</title>
		<link>http://eng.kulanov.org.ua/archives/85</link>
		<comments>http://eng.kulanov.org.ua/archives/85#comments</comments>
		<pubDate>Wed, 13 Jan 2010 07:46:52 +0000</pubDate>
		<dc:creator>Sergey Kulanov</dc:creator>
				<category><![CDATA[Matlab]]></category>
		<category><![CDATA[Software]]></category>
		<category><![CDATA[Statistics]]></category>
		<category><![CDATA[Bayes Rule]]></category>
		<category><![CDATA[Learning]]></category>
		<category><![CDATA[Machine Learning]]></category>

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		<description><![CDATA[The snapshot from the sideshow: ﻿﻿Click the image to enlarge Some information was taken from http://www.cs.bham.ac.uk/~axk/ML_new.htm]]></description>
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		<slash:comments>3</slash:comments>
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