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One of the simplest algorithms for sampling from a probability distribution is Random Walk Metropolis-Hastings. It proposes new samples by taking Gaussian-distributed steps, accepting or rejecting them to maintain the target distribution. I call this pdf the "fidget spinner".

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Alec Helbling

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79,869 просмотров • 1 год назад •via X (Twitter)

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Flow Matching aims to learn a "flow" that transforms a simple source distribution (e.g. Gaussian) to an arbitrarily complex target distribution. This video shows the evolution of the marginal probability path as a source distribution is transformed to a target distribution.
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Flow Matching aims to learn a "flow" that transforms a simple source distribution (e.g. Gaussian) to an arbitrarily complex target distribution. This video shows the evolution of the marginal probability path as a source distribution is transformed to a target distribution.

Alec Helbling

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A basic strategy for drawing from a random distribution is to first generate uniformly-distributed points, then "warp" these points so they're spread out according to the target distribution. For example, the Box-Muller transform takes uniform points to normally-distributed ones
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One common trick in MCMC methods is called 'burn-in'. It allows you to produce better samples by letting the chain first converge to the target distribution, discarding the first few samples which may be out of distribution.
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I tried to capture this fidget spinner using the Huawei Pura70 Ultra to test its capability in handling fast-moving subjects. The result is truly impressive.
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Israel is wiping out the bridges of South Lebanon — one by one. Not for military necessity, but to isolate an entire population. To trap them. To starve them. To cut them off from hospitals, food, and aid. Call it what it is: A crime against humanity.
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trying to show oomf the fidget spinner and he wouldn’t do it and it pissed me off
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