Density function approximation using reduced sufficient statistics for joint estimation of linear and nonlinear parameters

1999 ◽  
Vol 47 (8) ◽  
pp. 2089-2099 ◽  
Author(s):  
R.A. Iltis
Keyword(s):  
Density Function ◽  
Function Approximation ◽  
Joint Estimation ◽  
Sufficient Statistics ◽  
Nonlinear Parameters ◽  
Linear And Nonlinear ◽  
Density Function Approximation

A points estimation and series approximation method for uncertainty analysis

2009 ◽  
Vol 223 (9) ◽  
pp. 1997-2007 ◽  
Author(s):  
X F Zhang ◽  
Y E Zhao ◽  
Y M Zhang ◽  
X Z Huang ◽  
H Li
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Function Approximation ◽  
Structural Reliability ◽  
Estimation Method ◽  
Point Estimation ◽  
Performance Function ◽  
Density Function Approximation ◽  
Point Estimation Method

The objective of this article is to present an algorithm for moment evaluation and probability density function approximation of performance function for structural reliability analysis. In doing so, a point estimation method for probability moment of performance function is discussed at first. Based on the coherent relationship between the orthogonal polynomial and probability density function, formulas for point estimation are derived. Vector operators are defined to alleviate computational burden for computer programming. Then, by utilizing C-type Gram—Charlier series expansion method, a procedure for probability density function approximation of the performance function is studied. At last, the accuracy of the proposed method is demonstrated using three numerical examples.

scholarly journals Variation of Linear and Nonlinear Parameters in the Swim Strokes According to the Level of Expertise

Motor Control ◽  
2017 ◽  
Vol 21 (3) ◽  
pp. 312-326 ◽  
Author(s):  
Tiago M. Barbosa ◽  
Wan Xiu Goh ◽  
Jorge E. Morais ◽  
Mário J. Costa
Keyword(s):  
Nonlinear Parameters ◽  
Linear And Nonlinear

A novel mobile agent-based distributed evidential expectation maximization algorithm for uncertain sensor networks

2020 ◽  
pp. 014233122096958
Author(s):  
Mohiyeddin Mozaffari ◽  
Behrouz Safarinejadian ◽  
Mokhtar Shasadeghi
Keyword(s):  
Sensor Networks ◽  
Probability Density Function ◽  
Probability Density ◽  
Convergence Analysis ◽  
Density Function ◽  
Mobile Agent ◽  
Expectation Maximization ◽  
Convergence Criterion ◽  
Sufficient Statistics ◽  
Agent Based

In this paper, a novel mobile agent-based distributed evidential expectation maximization (MADEEM) algorithm is presented for sensor networks. The proposed algorithm is used for probability density function estimation and data clustering in the presence of uncertainties in sensor measurements. It is assumed that the sensor measurements are statistically modeled by a common Gaussian mixture model. The proposed algorithm maximizes a new generalized likelihood criterion in an iterative and distributed manner. For this purpose, mobile agents compute some local sufficient statistics by using local measurements of each sensor node. After the local computations, the global sufficient statistics are updated. At the end of iterations, the parameters of the probability density function are updated by using the global sufficient statistics. The mentioned process will be continued until the convergence criterion is satisfied. Convergence analysis of the proposed algorithm is also presented in this paper. After the convergence analysis, the simulation results show the promising performance of the proposed algorithm. Finally, the last part of the paper is devoted to the concluding remarks.

Sign in / Sign up

Export Citation Format

Share Document