why the em algorithm?
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Pure mathematics (theorems, definitions and proofs) is developed before the applications; e.g., why the number of processors is relatively prime to the number of memory locations\n\nAlgorithms are developed in a hope to improve something that already exists\n\nThe EM algorithm is used in computer vision as well as probability models like data clustering in machine learning \n\nEM stands for Expectation-Maximization where the likelihood of estimates is maximized\n\nThe study of Probability and Statistics involves learning and using many algorithms, including the EM algorithm
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Check out Markov Chain's.  You will find many uses for matrices.
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From a software developer point of view. This will be really hard to do and come up with a algorithm for seperating human voices from the regular music. They are sampled in digital files and to find which wave is the vioice is too hard for a computer to tell apart.
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try :\n\nwww.emp3finder.com\nwww.mp3shits.com
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Googling v.e.m.m.a. gives 195000 answers. \nYou have to be more precise in the question.
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Singular value decomposition and principal component analysis\n\n\nMichael E. Wall, Andreas Rechtsteiner, Luis M. Rocha\nModeling, Algorithms, and Informatics Group (CCS-3)\nLos Alamos National Laboratory, MS B256\nLos Alamos, New Mexico 87545, USA\n\n\n\nCitation: Wall, Michael E., Andreas Rechtsteiner, Luis M. Rocha."Singular value decomposition and principal component analysis". in A Practical Approach to Microarray Data Analysis. D.P. Berrar, W. Dubitzky, M. Granzow, eds. pp. 91-109, Kluwer: Norwell, MA (2003). LANL LA-UR-02-4001.\n\nAlso available in the arXiv.org e-Print archive and in Adobe Acrobat (.pdf) format. \n\nAbstract.\n\nThis chapter describes gene expression analysis by Singular Value Decomposition (SVD), emphasizing initial characterization of the data. We describe SVD methods for visualization of gene expression data, representation of the data using a smaller number of variables, and detection of patterns in noisy gene expression data. In addition, we describe the precise relation between SVD analysis and Principal Component Analysis (PCA) when PCA is calculated using the covariance matrix, enabling our descriptions to apply equally well to either method. Our aim is to provide definitions, interpretations, examples, and references that will serve as resources for understanding and extending the application of SVD and PCA to gene expression analysis.\n\nKeywords: bioinformatics, computational biology, linear algebra, data mining, singular value decomposition, principal component analysis, gene expression analysis, SVD, PCA, microarray analysis techniques.\n\n1. Introduction\nOne of the challenges of bioinformatics is to develop effective ways to analyze global gene expression data. A rigorous approach to gene expression analysis must involve an up-front characterization of the structure of the data. In addition to a broader utility in analysis methods, singular value decomposition (SVD) and principal component analysis (PCA) can be valuable tools in obtaining such a characterization. SVD and PCA are common techniques for analysis of multivariate data, and gene expression data are well suited to analysis using SVD/PCA. A single microarray  For simplicity, we use the term microarray to refer to all varieties of global gene expression technologies.\n\n\nClose experiment can generate measurements for thousands, or even tens of thousands of genes. Present experiments typically consist of less than ten assays, but can consist of hundreds (Hughes et al., 2000). Gene expression data are currently rather noisy, and SVD can detect and extract small signals from noisy data. \nThe goal of this chapter is to provide precise explanations of the use of SVD and PCA for gene expression analysis, illustrating methods using simple examples. We describe SVD methods for visualization of gene expression data, representation of the data using a smaller number of variables, and detection of patterns in noisy gene expression data. In addition, we describe the mathematical relation between SVD analysis and Principal Component Analysis (PCA) when PCA is calculated using the covariance matrix, enabling our descriptions to apply equally well to either method. Our aims are 1) to provide descriptions and examples of the application of SVD methods and interpretation of their results; 2) to establish a foundation for understanding previous applications of SVD to gene expression analysis; and 3) to provide interpretations and references to related work that may inspire new advances.\n\nIn section 1, the SVD is defined, with associations to other methods described. A summary of previous applications is presented in order to suggest directions for SVD analysis of gene expression data. In section 2 we discuss applications of SVD to gene expression analysis, including specific methods for SVD-based visualization of gene expression data, and use of SVD in detection of weak expression patterns. Some examples are given of previous applica
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they cause electromagnetic stroms
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http://www.neam.org/avindct2.htm\n\nhttp://www.neam.org/
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Visit this site, it should explain everything you've been asking:\n\nhttp://www.emc.maricopa.edu/faculty/farabee/BIOBK/BioBookPS.html
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i'm in the same boat as you, and there is only one surefire way to rem. e/t.: dirty lymmerics, it sounds crazy but it really works. now you just gotta find some
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This sounds like it came out of a textbook. What is the name of the various propagation models?
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Because up to a few years ago it was always the easiest and cheapest method to transmit and receive broadcasts with.  The first system invented (discovered ?) by Marconi was an analogue system and it has stayed that way since.  \n\nActually, the terms 'analogue' and 'digital' only apply to the modulation methods used;  once the modulated signal leaves the radiating antenna it travels as an analogue electro-magnetic wave through space regardless of which type of modulation is used.
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That sounds familiar... Think I'm working on it now! But I don't remember q. It is n in my book. In which case, q (if I'm thinking right) is the number of samples. Then again what's up with the ^'s? If np was > 10, then it meant that the distribution was approx. normal. You might be ahead of me right now...
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Gaussian distribution
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The Viterbi algorithm is a dynamic programming algorithm for finding the most likely sequence of hidden states – known as the Viterbi path – that result in a sequence of observed events, especially in the context of hidden Markov models. The forward algorithm is a closely related algorithm for computing the probability of a sequence of observed events. These algorithms form a subset of information theory.\n\nHere's an example:\n\nAssume you have a friend who lives far away and whom you call daily to talk about what each of you did that day. Your friend has only three things he's interested in: walking in the park, shopping, and cleaning his apartment. The choice of what to do is determined exclusively by the weather on a given day. You have no definite information about the weather where your friend lives, but you know general trends. Based on what he tells you he did each day, you try to guess what the weather must have been like.\n\nYou believe that the weather operates as a discrete Markov chain. There are two states, "Rainy" and "Sunny", but you cannot observe them directly, that is, they are hidden from you. On each day, there is a certain chance that your friend will perform one of the following activities, depending on the weather: "walk", "shop", or "clean". Since your friend tells you about his activities, those are the observations. The entire system is that of a hidden Markov model (HMM).\n\nYou know the general weather trends in the area and you know what your friend likes to do on average. In other words, the parameters of the HMM are known. In fact, you can write them down in the Python programming language:\n\nstates = ('Rainy', 'Sunny')\n\nobservations = ('walk', 'shop', 'clean')\n\nstart_probability = {'Rainy': 0.6, 'Sunny': 0.4}\n\ntransition_probability = {\n   'Rainy' : {'Rainy': 0.7, 'Sunny': 0.3},\n   'Sunny' : {'Rainy': 0.4, 'Sunny': 0.6},\n   }\n\nemission_probability = {\n   'Rainy' : {'walk': 0.1, 'shop': 0.4, 'clean': 0.5},\n   'Sunny' : {'walk': 0.6, 'shop': 0.3, 'clean': 0.1},\n   }\n\nIn this fragment, start_probability refers to your uncertainty about which state the HMM is in when your friend first calls you (all you know is that it tends to be rainy on average). The particular probability distribution used here is not the equilibrium one, which is (given the transition probabilities) actually approximately {'Rainy': 0.571, 'Sunny': 0.429}. The transition_probability refers to the change of the weather in the underlying Markov chain. In this example, there is only a 30% chance that tomorrow will be sunny if today is rainy. The emission_probability tells you how likely your friend is to perform a certain activity on each day. If it's rainy, there is a 50% chance that he is cleaning his apartment; if it's sunny, there is a 60% chance that he will go outside for a walk.\n\nYou talk to your friend three days in a row and discover that on the first day he went for a walk, on the second day he went shopping, and on the third day he cleaned his apartment. You have two questions: What is the overall probability of this sequence of observations? And what is the most likely sequence of rainy/sunny days that would explain these observations? The first question is answered by the forward algorithm; the second question is answered by the Viterbi algorithm. These two algorithms are structurally so similar (in fact, they are both instances of the same abstract algorithm) that they can be implemented in a single function:\n\ndef forward_viterbi(y, X, sp, tp, ep):\n   T = {}\n   for state in X:\n       ##          prob.      V. path  V. prob.\n       T[state] = (sp[state], [state], sp[state])\n   for output in y:\n       U = {}\n       for next_state in X:\n
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Go find a program called Emule.
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Don't do it!  EMI - electromagnetic interferrance.  The data signals in the cat5 cables will be corrupted.