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- W2474830748 abstract "Importance sampling is a technique originating in Monte Carlo simulation whereby one samples from a different, weighted distribution, in order to reduce variance of the resulting estimator. More recently, variations of importance sampling have emerged as a means for reducing computational and sample complexity in different problems of modern signal processing. Here we review importance sampling as it is manifested in three such problems: stochastic optimization, compressive sensing, and low-rank matrix approximation. In keeping with a general trend in convex optimization towards the analysis of phase transitions for exact recovery, importance sampling in compressive sensing and low-rank matrix recovery can be used to effectively push the phase transition for exact recovery towards fewer measurements." @default.
- W2474830748 created "2016-07-22" @default.
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- W2474830748 date "2015-01-01" @default.
- W2474830748 modified "2023-09-24" @default.
- W2474830748 title "Importance sampling in signal processing applications" @default.
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- W2474830748 doi "https://doi.org/10.1007/978-3-319-20188-7_8" @default.
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