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- W4310921854 abstract "Score based approaches to sampling have shown much success as a generative algorithm to produce new samples from a target density given a pool of initial samples. In this work, we consider if we have no initial samples from the target density, but rather $0^{th}$ and $1^{st}$ order oracle access to the log likelihood. Such problems may arise in Bayesian posterior sampling, or in approximate minimization of non-convex functions. Using this knowledge alone, we propose a Monte Carlo method to estimate the score empirically as a particular expectation of a random variable. Using this estimator, we can then run a discrete version of the backward flow SDE to produce samples from the target density. This approach has the benefit of not relying on a pool of initial samples from the target density, and it does not rely on a neural network or other black box model to estimate the score." @default.
- W4310921854 created "2022-12-21" @default.
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- W4310921854 creator A5058660064 @default.
- W4310921854 date "2022-12-06" @default.
- W4310921854 modified "2023-10-16" @default.
- W4310921854 title "Proposal of a Score Based Approach to Sampling Using Monte Carlo Estimation of Score and Oracle Access to Target Density" @default.
- W4310921854 doi "https://doi.org/10.48550/arxiv.2212.03325" @default.
- W4310921854 hasPublicationYear "2022" @default.
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