scholarly journals Adaptive optimal scaling of Metropolis–Hastings algorithms using the Robbins–Monro process

2016 ◽  
Vol 45 (17) ◽  
pp. 5098-5111 ◽  
Author(s):  
P. H. Garthwaite ◽  
Y. Fan ◽  
S. A. Sisson
Bernoulli ◽  
2014 ◽  
Vol 20 (4) ◽  
pp. 1930-1978 ◽  
Author(s):  
Benjamin Jourdain ◽  
Tony Lelièvre ◽  
Błażej Miasojedow

2001 ◽  
Vol 16 (4) ◽  
pp. 351-367 ◽  
Author(s):  
Gareth O. Roberts ◽  
Jeffrey S. Rosenthal

2021 ◽  
Vol 65 (4) ◽  
pp. 953-998
Author(s):  
Mark A. Iwen ◽  
Felix Krahmer ◽  
Sara Krause-Solberg ◽  
Johannes Maly

AbstractThis paper studies the problem of recovering a signal from one-bit compressed sensing measurements under a manifold model; that is, assuming that the signal lies on or near a manifold of low intrinsic dimension. We provide a convex recovery method based on the Geometric Multi-Resolution Analysis and prove recovery guarantees with a near-optimal scaling in the intrinsic manifold dimension. Our method is the first tractable algorithm with such guarantees for this setting. The results are complemented by numerical experiments confirming the validity of our approach.


2021 ◽  
Author(s):  
Lorenzo Scalera ◽  
Renato Vidoni ◽  
Andrea Giusti

Author(s):  
Yingdong Lu ◽  
Siva Theja Maguluri ◽  
Mark S. Squillante ◽  
Tonghoon Suk

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