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bartwronski.com | ||
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thenumb.at
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www.ethanepperly.com
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www.jeremykun.com
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| | | | | The singular value decomposition (SVD) of a matrix is a fundamental tool in computer science, data analysis, and statistics. It's used for all kinds of applications from regression to prediction, to finding approximate solutions to optimization problems. In this series of two posts we'll motivate, define, compute, and use the singular value decomposition to analyze some data. (Jump to the second post) I want to spend the first post entirely on motivation and background. | |
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djalil.chafai.net
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| | | Convergence in law to a constant. Let \( {{(X_n)}_{n\geq1}} \) be a sequence of random variables defined on a common probability space \( {(\Omega,\mathcal{A},\mathbb{P})} \), and taking their values in a metric space \( {(E,d)} \) equipped with its Borel sigma-field. It is well known that if \( {{(X_n)}_{n\geq1}} \) converges in law as \( {n\rightarrow\infty} \) to some Dirac... | ||
