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jdlm.info | ||
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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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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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haifengl.wordpress.com
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| | | Generative artificial intelligence (GenAI), especially ChatGPT, captures everyone's attention. The transformerbased large language models (LLMs), trained on a vast quantity of unlabeled data at scale, demonstrate the ability to generalize to many different tasks. To understand why LLMs are so powerful, we will deep dive into how they work in this post. LLM Evolutionary Tree... | ||
