Maximum a Posteriori Maximum Entropy Signal Denoising

2007 ◽  
Author(s):  
Abd-Krim Seghouane ◽  
Luc Knockaert ◽  
Kevin H. Knuth ◽  
Ariel Caticha ◽  
Julian L. Center ◽  
...  
Keyword(s):  
Maximum Entropy ◽  
Maximum A Posteriori ◽  
Signal Denoising ◽  
A Posteriori

scholarly journals Comparative Study between Generalized Maximum Entropy and Bayes Methods to Estimate the Four Parameter Weibull Growth Model

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Saifaldin Hashim Kamar ◽  
Basim Shlaibah Msallam
Keyword(s):  
Standard Deviation ◽  
Comparative Study ◽  
Maximum Entropy ◽  
Growth Model ◽  
Maximum A Posteriori ◽  
Computational Results ◽  
A Posteriori ◽  
Bayes Method ◽  
Bayes Methods ◽  
Generalized Maximum Entropy

The Weibull growth model is an important model especially for describing the growth instability; therefore, in this paper, three methods, namely, generalized maximum entropy, Bayes, and maximum a posteriori, for estimating the four parameter Weibull growth model have been presented and compared. To achieve this aim, it is necessary to use a simulation technique to generate the samples and perform the required comparisons, using varying sample sizes (10, 12, 15, 20, 25, and 30) and models depending on the standard deviation (0.5). It has been shown from the computational results that the Bayes method gives the best estimates.

Maximum a Posteriori Maximum Entropy HRF Estimation in Event Related fMRI

2009 ◽  
Author(s):  
Abd-Krim Seghouane ◽  
Paul M. Goggans ◽  
Chun-Yong Chan
Keyword(s):  
Maximum Entropy ◽  
Maximum A Posteriori ◽  
A Posteriori

scholarly journals Threshold Computation for Spatially Coupled Turbo-Like Codes on the AWGN Channel

Entropy ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. 240
Author(s):  
Muhammad Umar Farooq ◽  
Alexandre Graell i Amat ◽  
Michael Lentmaier
Keyword(s):  
Gaussian Noise ◽  
Belief Propagation ◽  
Additive White Gaussian Noise ◽  
Maximum A Posteriori ◽  
Prediction Methods ◽  
Threshold Analysis ◽  
A Posteriori ◽  
Awgn Channel ◽  
Efficient Prediction ◽  
Binary Erasure Channel

In this paper, we perform a belief propagation (BP) decoding threshold analysis of spatially coupled (SC) turbo-like codes (TCs) (SC-TCs) on the additive white Gaussian noise (AWGN) channel. We review Monte-Carlo density evolution (MC-DE) and efficient prediction methods, which determine the BP thresholds of SC-TCs over the AWGN channel. We demonstrate that instead of performing time-consuming MC-DE computations, the BP threshold of SC-TCs over the AWGN channel can be predicted very efficiently from their binary erasure channel (BEC) thresholds. From threshold results, we conjecture that the similarity of MC-DE and predicted thresholds is related to the threshold saturation capability as well as capacity-approaching maximum a posteriori (MAP) performance of an SC-TC ensemble.

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