Maximum Likelihood Estimation and non-linear least squares fitting with Levenberg-Marquardt Algorithm implementation in FPGA devices for high resolution hodoscopy

2012 ◽  
Author(s):  
Jose M. Blasco ◽  
Enrique Sanchis ◽  
Vicente Gonzalez ◽  
Jose D. Martin ◽  
Francisco J. Egea ◽  
...  
Keyword(s):  
Maximum Likelihood ◽  
High Resolution ◽  
Maximum Likelihood Estimation ◽  
Least Squares ◽  
Likelihood Estimation ◽  
Linear Least Squares ◽  
Least Squares Fitting ◽  
Marquardt Algorithm ◽  
Levenberg Marquardt ◽  
Algorithm Implementation

Maximum Likelihood Estimation and Non-Linear Least Squares Fitting Implementation in FPGA Devices for High Resolution Hodoscopy

2013 ◽  
Vol 60 (5) ◽  
pp. 3578-3584 ◽  
Author(s):  
Jose M. Blasco ◽  
Enrique Sanchis ◽  
Vicente Gonzalez ◽  
Jose D. Martin ◽  
Francisco J. Egea ◽  
...  
Keyword(s):  
Maximum Likelihood ◽  
High Resolution ◽  
Maximum Likelihood Estimation ◽  
Least Squares ◽  
Likelihood Estimation ◽  
Linear Least Squares ◽  
Least Squares Fitting ◽  
Non Linear

Jaya algorithm in estimation of P[X > Y] for two parameter Weibull distribution

2022 ◽  
Vol 7 (2) ◽  
pp. 2820-2839
Author(s):  
Saurabh L. Raikar ◽  
◽  
Dr. Rajesh S. Prabhu Gaonkar ◽  
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Weibull Distribution ◽  
Least Squares ◽  
Weighted Least Squares ◽  
Real Life ◽  
Likelihood Estimation ◽  
Estimation Methods ◽  
Jaya Algorithm ◽  
Two Parameter

<abstract> <p>Jaya algorithm is a highly effective recent metaheuristic technique. This article presents a simple, precise, and faster method to estimate stress strength reliability for a two-parameter, Weibull distribution with common scale parameters but different shape parameters. The three most widely used estimation methods, namely the maximum likelihood estimation, least squares, and weighted least squares have been used, and their comparative analysis in estimating reliability has been presented. The simulation studies are carried out with different parameters and sample sizes to validate the proposed methodology. The technique is also applied to real-life data to demonstrate its implementation. The results show that the proposed methodology's reliability estimates are close to the actual values and proceeds closer as the sample size increases for all estimation methods. Jaya algorithm with maximum likelihood estimation outperforms the other methods regarding the bias and mean squared error.</p> </abstract>

Determining Extrinsic Parameters for Active Stereovision

2009 ◽  
Vol 13 (2) ◽  
pp. 76-79 ◽  
Author(s):  
Huawei Wang ◽  
◽  
De Xu
Keyword(s):  
Least Squares ◽  
Rotational Axis ◽  
The Novel ◽  
Mapping Model ◽  
Least Squares Fitting ◽  
Marquardt Algorithm ◽  
Levenberg Marquardt ◽  
Extrinsic Parameters ◽  
Novel Method ◽  
The Relationship

In the novel method we propose for determining extrinsic parameters for active stereovision, we first map the relationship between rotational and yaw angles based on least squares fitting, then optimize the rotational axis between two cameras using the Levenberg-Marquardt algorithm. Extrinsic parameters are then easily derived for active stereovision based on the mapping model without complex recalibration. The results of experiments confirmed our proposed method's feasibility.

scholarly journals "Evaluation of Some Methods for Estimating the Weibull Distribution Parameters"

2017 ◽  
Vol 4 (2) ◽  
pp. 8-14
Author(s):  
J. A. Labban ◽  
H. H. Depheal
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Weibull Distribution ◽  
Least Squares ◽  
Mean Square Error ◽  
Random Sample ◽  
Likelihood Estimation ◽  
Mean Square ◽  
Distribution Parameters

"This paper some of different methods to estimate the parameters of the 2-Paramaters Weibull distribution such as (Maximum likelihood Estimation, Moments, Least Squares, Term Omission). Mean square error will be considered to compare methods fits in case to select the best one. There by simulation will be implemented to generate different random sample of the 2-parameters Weibull distribution, those contain (n=10, 50, 100, 200) iteration each 1000 times."

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