Optimal sampling schemes for the Gaussian hypothesis testing problem

1990 ◽  
Vol 38 (10) ◽  
pp. 1677-1686 ◽  
Author(s):  
R.K. Bahr ◽  
J.A. Bucklew
2018 ◽  
Vol 7 (3) ◽  
pp. 1 ◽  
Author(s):  
Hatem Baffoun ◽  
Mekki Hajlaoui ◽  
Abdeljelil Farhat

In this paper, we compare empirically the performance of some adaptive MCMC methods, that is, Adaptive Metropolis (AM) algorithm, Single Component Adaptive Metropolis (SCAM) algorithm and Delayed Rejection Adaptive Metropolis (DRAM) algorithm. The context is the simulation of non-standard discrete distributions. The performance criterion used is the precision of the frequency estimator. An application to a Bayesian hypothesis testing problem shows the superiority of the DRAM algorithm over the other considered sampling schemes.


Symmetry ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 942 ◽  
Author(s):  
Vali Soltani Masih ◽  
Stanisława Kanas

Let ST L ( s ) and CV L ( s ) denote the family of analytic and normalized functions f in the unit disk D : = z : | z | < 1 , such that the quantity z f ′ ( z ) / f ( z ) or 1 + z f ″ ( z ) / f ′ ( z ) respectively are lying in the region bounded by the limaçon ( u − 1 ) 2 + v 2 − s 4 2 = 4 s 2 u − 1 + s 2 2 + v 2 , where 0 < s ≤ 1 / 2 . The limaçon of Pascal is a curve that possesses properties which qualify it for the several applications in mathematics, statistics (hypothesis testing problem) but also in mechanics (fluid processing applications, known limaçon technology is employed to extract electrical power from low-grade heat, etc.). In this paper we present some results concerning the behavior of f on the classes ST L ( s ) or CV L ( s ) . Some appropriate examples are given.


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