Macroscopic Analysis of Vector Approximate Message Passing in a Model-Mismatched Setting

In this study, macroscopic properties of the vector approximate message passing (VAMP) algorithm for inference of generalized linear models are investigated using a non-rigorous heuristic method of statistical mechanics when the true posterior cannot be used and the measurement matrix is a sample fr...

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Vydané v:	IEEE transactions on information theory Ročník 68; číslo 8; s. 5579 - 5600
Hlavní autori:	Takahashi, Takashi, Kabashima, Yoshiyuki
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.08.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Analytical models Approximation algorithms compressive sensing Empirical analysis Generalized linear models Heuristic algorithms Heuristic methods Inference Inference algorithms Invariants Mathematical models Matrices (mathematics) Message passing Message passing algorithms Optimization Performance assessment phase transitions random matrices Rotation Rotation measurement Statistical mechanics Statistical models Symmetry
ISSN:	0018-9448, 1557-9654
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