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	<title>Neurobot &#187; Tutorials</title>
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	<link>http://neurobot.bio.auth.gr</link>
	<description>A computational neuroscience and neuroinformatics blog</description>
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		<title>Information Theory: A Tutorial Introduction</title>
		<link>http://neurobot.bio.auth.gr/2018/information-theory-a-tutorial-introduction/</link>
		<comments>http://neurobot.bio.auth.gr/2018/information-theory-a-tutorial-introduction/#comments</comments>
		<pubDate>Tue, 20 Feb 2018 15:39:29 +0000</pubDate>
		<dc:creator>Dimitrios A. Adamos</dc:creator>
				<category><![CDATA[Tutorials]]></category>
		<category><![CDATA[Information Theory]]></category>

		<guid isPermaLink="false">http://neurobot.bio.auth.gr/?p=4015</guid>
		<description><![CDATA[Shannon&#8217;s mathematical theory of communication defines fundamental limits on how much information can be transmitted between the different components of any man-made or biological system. This paper is an informal but rigorous introduction to the main ideas implicit in Shannon&#8217;s theory. An annotated reading list is provided for further reading. https://arxiv.org/pdf/1802.05968]]></description>
		<wfw:commentRss>http://neurobot.bio.auth.gr/2018/information-theory-a-tutorial-introduction/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
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		<item>
		<title>Dominant-Sets clustering for spike sorting</title>
		<link>http://neurobot.bio.auth.gr/2013/dominant-sets-clustering-for-spike-sorting/</link>
		<comments>http://neurobot.bio.auth.gr/2013/dominant-sets-clustering-for-spike-sorting/#comments</comments>
		<pubDate>Wed, 30 Jan 2013 20:46:48 +0000</pubDate>
		<dc:creator>Dimitrios A. Adamos</dc:creator>
				<category><![CDATA[Software]]></category>
		<category><![CDATA[Tutorials]]></category>
		<category><![CDATA[Clustering]]></category>
		<category><![CDATA[Sparse neurons]]></category>
		<category><![CDATA[Spike Sorting]]></category>

		<guid isPermaLink="false">http://neurobot.bio.auth.gr/?p=3353</guid>
		<description><![CDATA[The decision about the actual number of active neurons is an open issue in spike sorting, with sparsely firing neurons and background activity the most influencing factors. Dominant-sets clustering algorithm is a graph-theoretical algorithmic procedure that successfully addresses this issue. The quality of grouping in the data is evaluated with the estimation of &#8216;cohesiveness&#8217;, i.e. [...]]]></description>
		<wfw:commentRss>http://neurobot.bio.auth.gr/2013/dominant-sets-clustering-for-spike-sorting/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>Using ISOMAP algorithm for feature extraction in spike sorting</title>
		<link>http://neurobot.bio.auth.gr/2013/using-isomap-algorithm-for-feature-extraction-in-spike-sorting/</link>
		<comments>http://neurobot.bio.auth.gr/2013/using-isomap-algorithm-for-feature-extraction-in-spike-sorting/#comments</comments>
		<pubDate>Wed, 30 Jan 2013 20:02:43 +0000</pubDate>
		<dc:creator>Dimitrios A. Adamos</dc:creator>
				<category><![CDATA[Software]]></category>
		<category><![CDATA[Tutorials]]></category>
		<category><![CDATA[Dimensionality Reduction]]></category>
		<category><![CDATA[Sparse neurons]]></category>
		<category><![CDATA[Spike Sorting]]></category>

		<guid isPermaLink="false">http://neurobot.bio.auth.gr/?p=3326</guid>
		<description><![CDATA[Background noise and spike overlap pose problems in contemporary spike-sorting strategies. The (non-linear) isometric feature mapping (ISOMAP) technique reveals the intrinsic data structure and helps with recognising the involved neurons. To reproduce this tutorial in MATLAB you will need : 1. ISOMAP source code for MATLAB (for more information and updated version see here: http://isomap.stanford.edu) [...]]]></description>
		<wfw:commentRss>http://neurobot.bio.auth.gr/2013/using-isomap-algorithm-for-feature-extraction-in-spike-sorting/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>An Expectation-Maximization tutorial in neural signal analysis</title>
		<link>http://neurobot.bio.auth.gr/2011/an-expectation-maximization-tutorial-in-neural-signal-processing/</link>
		<comments>http://neurobot.bio.auth.gr/2011/an-expectation-maximization-tutorial-in-neural-signal-processing/#comments</comments>
		<pubDate>Mon, 03 Jan 2011 11:18:06 +0000</pubDate>
		<dc:creator>Dimitrios A. Adamos</dc:creator>
				<category><![CDATA[Tutorials]]></category>
		<category><![CDATA[Clustering]]></category>
		<category><![CDATA[Spike Sorting]]></category>
		<category><![CDATA[Statistical Analysis]]></category>

		<guid isPermaLink="false">http://neurobot.bio.auth.gr/?p=2811</guid>
		<description><![CDATA[In this tutorial by Dr. Liam Paninski, the Expectation-Maximization (EM) algorithm is discussed and illustrated in a variety of neural examples. Key topics addressed: Example: Mixture models and spike sorting The method of bound optimization via auxiliary functions provides a useful alternative optimization technique The EM algorithm for maximizing the likelihood given hidden data may be derived [...]]]></description>
		<wfw:commentRss>http://neurobot.bio.auth.gr/2011/an-expectation-maximization-tutorial-in-neural-signal-processing/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>Tutorial on Geometrical Data Analysis: Algorithms for Vectorial Pattern-Analysis</title>
		<link>http://neurobot.bio.auth.gr/2010/tutorial-on-geometrical-data-analysis-algorithms-for-vectorial-pattern-analysis/</link>
		<comments>http://neurobot.bio.auth.gr/2010/tutorial-on-geometrical-data-analysis-algorithms-for-vectorial-pattern-analysis/#comments</comments>
		<pubDate>Tue, 28 Dec 2010 12:10:14 +0000</pubDate>
		<dc:creator>Dimitrios A. Adamos</dc:creator>
				<category><![CDATA[Tutorials]]></category>
		<category><![CDATA[Clustering]]></category>
		<category><![CDATA[Dimensionality Reduction]]></category>
		<category><![CDATA[Neural Networks]]></category>

		<guid isPermaLink="false">http://neurobot.bio.auth.gr/?p=2787</guid>
		<description><![CDATA[By Dr. Nikolaos A. Laskaris The term ‘‘pattern’’, currently, encompasses the notion of a variety of data-forms the machines have to tackle with. Despite the fact that in early days it was used mostly for pictorial information, i.e. 2D-signals, now the same term stands almost for any output from a data-source. For instance, any digital-signal can be [...]]]></description>
		<wfw:commentRss>http://neurobot.bio.auth.gr/2010/tutorial-on-geometrical-data-analysis-algorithms-for-vectorial-pattern-analysis/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>Manifold learning examples</title>
		<link>http://neurobot.bio.auth.gr/2010/manifold-learning-examples/</link>
		<comments>http://neurobot.bio.auth.gr/2010/manifold-learning-examples/#comments</comments>
		<pubDate>Thu, 16 Sep 2010 12:32:11 +0000</pubDate>
		<dc:creator>Dimitrios A. Adamos</dc:creator>
				<category><![CDATA[Tutorials]]></category>
		<category><![CDATA[Dimensionality Reduction]]></category>

		<guid isPermaLink="false">http://neurobot.bio.auth.gr/?p=2440</guid>
		<description><![CDATA[A tutorial on PCA, LLE and ISOMAP by Dan Ventura (2008). You may access the tutorial here or visit the author&#8217;s &#8220;Advanced Neural Networks and Machine Learning&#8221; course home page.]]></description>
		<wfw:commentRss>http://neurobot.bio.auth.gr/2010/manifold-learning-examples/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>Signal-to-noise ratio in neuroscience</title>
		<link>http://neurobot.bio.auth.gr/2009/signal-to-noise-ratio-in-neuroscience/</link>
		<comments>http://neurobot.bio.auth.gr/2009/signal-to-noise-ratio-in-neuroscience/#comments</comments>
		<pubDate>Sat, 05 Dec 2009 20:53:27 +0000</pubDate>
		<dc:creator>Dimitrios A. Adamos</dc:creator>
				<category><![CDATA[Tutorials]]></category>
		<category><![CDATA[Information Theory]]></category>
		<category><![CDATA[Neurophysiology]]></category>

		<guid isPermaLink="false">http://neurobot.bio.auth.gr/?p=2379</guid>
		<description><![CDATA[By Simon R Schultz (2007), Scholarpedia, 2(6):2046 Signal-to-Noise ratio (SNR) generically means the dimensionless ratio of signal power to noise power. It has a long history of being used in neuroscience as a measure of the fidelity of signal transmission and detection by neurons and synapses. A common use of SNR is to compare the [...]]]></description>
		<wfw:commentRss>http://neurobot.bio.auth.gr/2009/signal-to-noise-ratio-in-neuroscience/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>Statistical Data Mining Tutorials</title>
		<link>http://neurobot.bio.auth.gr/2009/statistical-data-mining-tutorials/</link>
		<comments>http://neurobot.bio.auth.gr/2009/statistical-data-mining-tutorials/#comments</comments>
		<pubDate>Fri, 19 Jun 2009 08:47:11 +0000</pubDate>
		<dc:creator>Dimitrios A. Adamos</dc:creator>
				<category><![CDATA[Tutorials]]></category>
		<category><![CDATA[Statistical Analysis]]></category>

		<guid isPermaLink="false">http://neurobot.bio.auth.gr/?p=2334</guid>
		<description><![CDATA[The following set of tutorials focus on many aspects of statistical data mining, including the foundations of probability, the foundations of statistical data analysis, and most of the classic machine learning and data mining algorithms. These include classification algorithms such as decision trees, neural nets, Bayesian classifiers, Support Vector Machines and cased-based (aka non-parametric) learning. [...]]]></description>
		<wfw:commentRss>http://neurobot.bio.auth.gr/2009/statistical-data-mining-tutorials/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>New datasharing website for computational neuroscience</title>
		<link>http://neurobot.bio.auth.gr/2008/new-datasharing-website-for-computational-neuroscience/</link>
		<comments>http://neurobot.bio.auth.gr/2008/new-datasharing-website-for-computational-neuroscience/#comments</comments>
		<pubDate>Fri, 04 Apr 2008 12:32:22 +0000</pubDate>
		<dc:creator>Dimitrios A. Adamos</dc:creator>
				<category><![CDATA[Software]]></category>
		<category><![CDATA[Tutorials]]></category>
		<category><![CDATA[Neurophysiology]]></category>
		<category><![CDATA[Statistical Analysis]]></category>

		<guid isPermaLink="false">http://neurobot.bio.auth.gr/?p=2167</guid>
		<description><![CDATA[The new website http://www.crcns.org is available for sharing resources for computational neuroscience, such as high-quality experimental data sets, analytical tools and models. Currently, the website hosts resources whose preparation have been supported by a new funding track in the joint NSF/NIH program Collaborative Research in Computational Neuroscience. http://www.nsf.gov/funding/pgm_summ.jsp?pims_id=5147 The resources currently available are electrophysiology data [...]]]></description>
		<wfw:commentRss>http://neurobot.bio.auth.gr/2008/new-datasharing-website-for-computational-neuroscience/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>An Introduction to Wavelets</title>
		<link>http://neurobot.bio.auth.gr/2006/an-introduction-to-wavelets/</link>
		<comments>http://neurobot.bio.auth.gr/2006/an-introduction-to-wavelets/#comments</comments>
		<pubDate>Tue, 05 Sep 2006 16:27:50 +0000</pubDate>
		<dc:creator>Dimitrios A. Adamos</dc:creator>
				<category><![CDATA[Software]]></category>
		<category><![CDATA[Tutorials]]></category>
		<category><![CDATA[Wavelets]]></category>

		<guid isPermaLink="false">http://neurobot.bio.auth.gr/?p=2015</guid>
		<description><![CDATA[&#8220;Wavelets are mathematical functions that cut up data into different frequency components, and then study each component with a resolution matched to its scale. They have advantages over traditional Fourier methods in analyzing physical situations where the signal contains discontinuities and sharp spikes.&#8221; This paper introduces wavelets to the interested technical person outside of the [...]]]></description>
		<wfw:commentRss>http://neurobot.bio.auth.gr/2006/an-introduction-to-wavelets/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>The Wavelet Tutorial: The Engineer&#8217;s Ultimate Guide to Wavelet Analysis</title>
		<link>http://neurobot.bio.auth.gr/2006/the-wavelet-tutorial-the-engineers-ultimate-guide-to-wavelet-analysis/</link>
		<comments>http://neurobot.bio.auth.gr/2006/the-wavelet-tutorial-the-engineers-ultimate-guide-to-wavelet-analysis/#comments</comments>
		<pubDate>Fri, 18 Aug 2006 07:10:03 +0000</pubDate>
		<dc:creator>Dimitrios A. Adamos</dc:creator>
				<category><![CDATA[Tutorials]]></category>
		<category><![CDATA[Wavelets]]></category>

		<guid isPermaLink="false">http://neurobot.bio.auth.gr/?p=2014</guid>
		<description><![CDATA[An excellent tutorial on Wavelet Analysis covering also the basic concepts of mathematical transformations, time-frequency representations and non-stationary signal properties. By Robi Polikar Dept. of Electrical and Computer Engineering Rowan University Part I of this tutorial presents an overview of the basic concepts that are of importance in understanding the wavelet theory. This part summarizes [...]]]></description>
		<wfw:commentRss>http://neurobot.bio.auth.gr/2006/the-wavelet-tutorial-the-engineers-ultimate-guide-to-wavelet-analysis/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>A Cluster Analysis Tutorial</title>
		<link>http://neurobot.bio.auth.gr/2006/a-cluster-analysis-tutorial/</link>
		<comments>http://neurobot.bio.auth.gr/2006/a-cluster-analysis-tutorial/#comments</comments>
		<pubDate>Wed, 08 Mar 2006 18:52:10 +0000</pubDate>
		<dc:creator>Dimitrios A. Adamos</dc:creator>
				<category><![CDATA[Tutorials]]></category>
		<category><![CDATA[Clustering]]></category>

		<guid isPermaLink="false">http://neurobot.bio.auth.gr/?p=2000</guid>
		<description><![CDATA[The term cluster analysis (first used by Tryon, 1939) encompasses a number of different algorithms and methods for grouping objects of similar kind into respective categories. A general question facing researchers in many areas of inquiry is how to organize observed data into meaningful structures, that is, to develop taxonomies. In other words cluster analysis [...]]]></description>
		<wfw:commentRss>http://neurobot.bio.auth.gr/2006/a-cluster-analysis-tutorial/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>A tutorial on Cross Correlation  &amp; Joint Peristimulus Time Histogram (JPSTH)</title>
		<link>http://neurobot.bio.auth.gr/2005/a-tutorial-on-cross-correlation-joint-peristimulus-time-histogram-jpsth/</link>
		<comments>http://neurobot.bio.auth.gr/2005/a-tutorial-on-cross-correlation-joint-peristimulus-time-histogram-jpsth/#comments</comments>
		<pubDate>Sat, 24 Sep 2005 17:29:18 +0000</pubDate>
		<dc:creator>Dimitrios A. Adamos</dc:creator>
				<category><![CDATA[Tutorials]]></category>
		<category><![CDATA[Statistical Analysis]]></category>

		<guid isPermaLink="false">http://neurobot.bio.auth.gr/?p=1995</guid>
		<description><![CDATA[This section provides an introduction to the analysis of data obtained from using small extracellular electrodes to record neural activity. There&#8217;s a great deal of interesting stuff to be learned from analyzing simultaneously recorded spike trains. Probably the most popular analysis involves the construction of the crosscorrelograms and JPSTHs. MULAB, University of Pennsylvania provides an [...]]]></description>
		<wfw:commentRss>http://neurobot.bio.auth.gr/2005/a-tutorial-on-cross-correlation-joint-peristimulus-time-histogram-jpsth/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>A Simple Matlab Data Analysis Tutorial</title>
		<link>http://neurobot.bio.auth.gr/2005/a-simple-matlab-data-analysis-tutorial/</link>
		<comments>http://neurobot.bio.auth.gr/2005/a-simple-matlab-data-analysis-tutorial/#comments</comments>
		<pubDate>Sun, 05 Jun 2005 12:01:27 +0000</pubDate>
		<dc:creator>Dimitrios A. Adamos</dc:creator>
				<category><![CDATA[Tutorials]]></category>
		<category><![CDATA[Statistical Analysis]]></category>

		<guid isPermaLink="false">http://neurobot.bio.auth.gr/?p=1981</guid>
		<description><![CDATA[A Matlab Data Analysis Tutorial for use on Computational Neuroscience methods. Assembled, edited and written by: Oren Shriki, oren.shriki@weizmann.ac.il Oren Farber, orenf@cc.huji.ac.il Dmitri Bibitchkov, dmitri.bibitchkov@weizmann.ac.il Access the Tutorial Here]]></description>
		<wfw:commentRss>http://neurobot.bio.auth.gr/2005/a-simple-matlab-data-analysis-tutorial/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>Advanced Statistics with MATLAB</title>
		<link>http://neurobot.bio.auth.gr/2005/advanced-statistics-with-matlab/</link>
		<comments>http://neurobot.bio.auth.gr/2005/advanced-statistics-with-matlab/#comments</comments>
		<pubDate>Sat, 04 Jun 2005 12:43:33 +0000</pubDate>
		<dc:creator>Dimitrios A. Adamos</dc:creator>
				<category><![CDATA[Tutorials]]></category>
		<category><![CDATA[Statistical Analysis]]></category>

		<guid isPermaLink="false">http://neurobot.bio.auth.gr/?p=1980</guid>
		<description><![CDATA[The purpose of this tutorial is to present several advanced statistics techniques using Matlab Statistics toolbox. Topics discussed in this tutorial include: 1. Covariance matrices and Eigenvalues 2. Principal component analysis 3. Canonical Correlation 4. Polynomial fit for a set of points Access this Tutorial Here By Daniela Raicu draicu@cs.depaul.edu School of Computer Science, Telecommunications, [...]]]></description>
		<wfw:commentRss>http://neurobot.bio.auth.gr/2005/advanced-statistics-with-matlab/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>Statistics with MATLAB</title>
		<link>http://neurobot.bio.auth.gr/2005/statistics-with-matlab/</link>
		<comments>http://neurobot.bio.auth.gr/2005/statistics-with-matlab/#comments</comments>
		<pubDate>Fri, 03 Jun 2005 12:39:15 +0000</pubDate>
		<dc:creator>Dimitrios A. Adamos</dc:creator>
				<category><![CDATA[Tutorials]]></category>
		<category><![CDATA[Statistical Analysis]]></category>

		<guid isPermaLink="false">http://neurobot.bio.auth.gr/?p=1979</guid>
		<description><![CDATA[The purpose of this tutorial is to present several statistics techniques using Matlab Statistics toolbox. Topics discussed in this tutorial include: 1. Descriptive statistics 2. Linear Models 3. Cluster analysis 4. Principal component analysis Access this Tutorial Here By Daniela Raicu draicu@cs.depaul.edu School of Computer Science, Telecommunications, and Information Systems DePaul University, Chicago, IL 60604]]></description>
		<wfw:commentRss>http://neurobot.bio.auth.gr/2005/statistics-with-matlab/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>A Tutorial on Principal Components Analysis</title>
		<link>http://neurobot.bio.auth.gr/2005/a-tutorial-on-principal-components-analysis/</link>
		<comments>http://neurobot.bio.auth.gr/2005/a-tutorial-on-principal-components-analysis/#comments</comments>
		<pubDate>Tue, 22 Feb 2005 11:01:18 +0000</pubDate>
		<dc:creator>Dimitrios A. Adamos</dc:creator>
				<category><![CDATA[Tutorials]]></category>
		<category><![CDATA[Dimensionality Reduction]]></category>

		<guid isPermaLink="false">http://neurobot.bio.auth.gr/?p=1974</guid>
		<description><![CDATA[A tutorial on Principal Components Analysis Lindsay I Smith February 26, 2002 This tutorial is designed to give the reader an understanding of Principal Components Analysis (PCA). PCA is a useful statistical technique that has found application in fields such as face recognition and image compression, and is a common technique for finding patterns in [...]]]></description>
		<wfw:commentRss>http://neurobot.bio.auth.gr/2005/a-tutorial-on-principal-components-analysis/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>Tutorial on Principal Component Analysis</title>
		<link>http://neurobot.bio.auth.gr/2005/tutorial-on-principal-component-analysis/</link>
		<comments>http://neurobot.bio.auth.gr/2005/tutorial-on-principal-component-analysis/#comments</comments>
		<pubDate>Tue, 22 Feb 2005 10:45:09 +0000</pubDate>
		<dc:creator>Dimitrios A. Adamos</dc:creator>
				<category><![CDATA[Tutorials]]></category>
		<category><![CDATA[Dimensionality Reduction]]></category>

		<guid isPermaLink="false">http://neurobot.bio.auth.gr/?p=1973</guid>
		<description><![CDATA[A theoretical Tutorial on Principal Component Analysis. by Javier R. Movellan. Read the full document Copyright 1997, 2003 Javier R. Movellan. This is an open source document. Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.2 or any later version published by the [...]]]></description>
		<wfw:commentRss>http://neurobot.bio.auth.gr/2005/tutorial-on-principal-component-analysis/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>A Tutorial on Principal Component Analysis</title>
		<link>http://neurobot.bio.auth.gr/2005/a-tutorial-on-principal-component-analysis/</link>
		<comments>http://neurobot.bio.auth.gr/2005/a-tutorial-on-principal-component-analysis/#comments</comments>
		<pubDate>Tue, 22 Feb 2005 09:49:52 +0000</pubDate>
		<dc:creator>Dimitrios A. Adamos</dc:creator>
				<category><![CDATA[Tutorials]]></category>
		<category><![CDATA[Dimensionality Reduction]]></category>

		<guid isPermaLink="false">http://neurobot.bio.auth.gr/?p=1972</guid>
		<description><![CDATA[A Tutorial On Principal Component Analysis Derivation, Discussion and Singular Value Decomposition. Jon Shlens &#124; jonshlens@ucsd.edu 25 March 2003 &#124; Version 1 Principal component analysis (PCA) is a mainstay of modern data analysis &#8211; a black box that is widely used but poorly understood. The goal of this paper is to dispel the magic behind [...]]]></description>
		<wfw:commentRss>http://neurobot.bio.auth.gr/2005/a-tutorial-on-principal-component-analysis/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>Introduction to Probability Theory and Statistics</title>
		<link>http://neurobot.bio.auth.gr/2005/introduction-to-probability-theory-and-statistics/</link>
		<comments>http://neurobot.bio.auth.gr/2005/introduction-to-probability-theory-and-statistics/#comments</comments>
		<pubDate>Tue, 22 Feb 2005 09:17:32 +0000</pubDate>
		<dc:creator>Dimitrios A. Adamos</dc:creator>
				<category><![CDATA[Tutorials]]></category>
		<category><![CDATA[Statistical Analysis]]></category>

		<guid isPermaLink="false">http://neurobot.bio.auth.gr/?p=1970</guid>
		<description><![CDATA[Introduction to Probability Theory and Statistic by Javier R. Movellan. Read the full document Copyright 1996,1998, 2002 Javier R. Movellan. This is an open source document. Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.2 or any later version published by the Free [...]]]></description>
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