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jdlm.info | ||
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healeycodes.com
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| | | | | Generating random but familiar text by building Markov chains from scratch. | |
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setosa.io
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www.ethanepperly.com
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| | | | | [AI summary] The user is discussing Markov Chain Monte Carlo (MCMC) methods, specifically the Metropolis-Hastings algorithm, applied to sampling from a distribution defined by a matrix $ A $. The focus is on the acceptance probability when transitioning between subsets $ S $ and $ S' $ of size $ k $, where the acceptance probability is determined by the ratio of determinants of submatrices of $ A $. The user is also exploring the computational complexity of these methods and their application to problems involving large matrices. | |
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jaketae.github.io
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| | | Note: This blog post was completed as part of Yale's CPSC 482: Current Topics in Applied Machine Learning. | ||
