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ASarxiv-stat-ml.bsky.social

Oussama Zekri, Ambroise Odonnat, Abdelhakim Benechehab, Linus Bleistein, Nicolas Boull\'e, Ievgen Redko Large Language Models as Markov Chains https://arxiv.org/abs/2410.02724

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Importance is Important: Generalized Markov Chain Importance Sampling Methods https://arxiv.org/abs/2304.06251 arXiv:2304.06251v2 Announce Type: replace-cross Abstract: We show that for any multiple-try Metropolis algorithm, one can always accept the proposal and evaluate the importance weight t 📈🤖

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FPmidnightrush.bsky.social

i could make a trans girl traumaqueer Markov chain and it would 100% pass the turing test

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Debiased Distribution Compression https://arxiv.org/abs/2404.12290 arXiv:2404.12290v1 Announce Type: cross Abstract: Modern compression methods can summarize a target distribution $\mathbb{P}$ more succinctly than i.i.d. sampling but require access to a low-bias input sequence like a Markov chai 📈🤖

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Control Variate-based Stochastic Sampling from the Probability Simplex https://arxiv.org/abs/2410.00845 arXiv:2410.00845v1 Announce Type: new Abstract: This paper presents a control variate-based Markov chain Monte Carlo algorithm for efficient sampling from the probability simplex, with a focus 📈🤖

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JSjamessteeleii.bsky.social

Now, the parameter selection all seems reasonable to me coming from sources I'd have probably gone to were I building this model myself... but, there's not much detail about the actual model specification in the paper itself (just vaguely says "Markov model")...

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JSjamessteeleii.bsky.social

Firstly, just in case people aren't aware because the title could be misleading, this is essentially a simulation study using a Markov model taking advantage of life table data, registries, and RCTs for parameter estimate selection...

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ACarxiv-cs-cc.bsky.social

Andreas G\"obel, Marcus Pappik Lazy brute-force sampling: A universal perfect sampling scheme from Markov chains https://arxiv.org/abs/2410.00882

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