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blog.jordan.matelsky.com | ||
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djalil.chafai.net
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| | | | | Markov-Chains-Monte-Carlo (MCMC for short) methods are widely used in practice for the approximate computation of integrals on various types of spaces. More precisely, let \(\mu\) be a probability measure on \(E\), known only up to a multiplicative constant. Let \(K\) be an irreducible Markov kernel on \(E\). Then by using a classical Metropolis-Hastings type construction, one cook up a computable... | |
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sookocheff.com
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| | | | | A common method of reducing the complexity of n-gram modeling is using the Markov Property. The Markov Property states that the probability of future states depends only on the present state, not on the sequence of events that preceded it. This concept can be elegantly implemented using a Markov Chain storing the probabilities of transitioning to a next state. | |
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setosa.io
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| | | | | [AI summary] A visual and practical explanation of Markov Chains, covering concepts like state spaces and transition matrices, with examples in weather modeling and Google's search algorithm. | |
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www.jamesserra.com
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| | | [AI summary] This technical blog post explains the concepts behind LLMs and Generative AI, details their architecture, and guides developers on using Azure and Microsoft Copilot Studio to apply these models to enterprise data via RAG techniques. | ||
