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About 10,000 papers for “parameter estimation”

Parameter Estimation

Thorsten Clevorn, Johannes Brauers, Marc Adrat et al. · 2013

Abstract — We propose the combination of iterative demodulation and iterative source-channel decoding as a multiple Turbo process. The receiver structures of bit-interleaved coded modulation with iterative decoding (BICM-ID), iterative sourcechannel decoding (ISCD), and iterative source coded modulation (ISCM) are merged to one novel Turbo system, in which in two iterative loops reliability information is exchanged between the three single components, demodulator, channel decoder, and (softbit) source decoder. Simulations show quality improvements compared to the different previously known systems, which use iterative processing only for two components of the receiver.

Cited by 0

Parameter Estimation.

Paul Akula · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · 2019

Abstract not provided.

Cited by 0Open access

Parameter Estimation

Robert Dürr · 2018

This chapter focuses on online estimation and application of artificial neural networks (ANNs). The step known as parameter estimation aims at choosing the unknown model parameters such that the model behavior accurately represents the process. This usually involves the solution of a so-called inverse problem where model parameters are adapted to optimize a certain measure or cost function. Practical identifiability analysis gives information of the quality with which parameters can be estimated from the available noise corrupted and discrete measurements. The main goal of any parameter estimation procedure is the adaption of the vector of unknown model parameters such that the model accurately represents the real process by solving the so-called inverse problem. If a parameter estimate is characterized by large uncertainty in terms of a large confidence interval, it is said to be practically non-identifiable. In contrast, if an estimated parameter has a small confidence interval, it is said to be practically identifiable.

Cited by 1

Parameter estimation.

Jolene Atia (3172896), Conor McCloskey (3172887), Anatoly S. Shmygol (3172893) et al. · Figshare · 2016

Cited by 0Open access

Parameter Estimation

David W. Scott · Statistics · 2020

This chapter focuses on parametric probability density estimation, assuming the statistician or subject matter expert has correctly selected the appropriate choice for the probability density function (PDF). An estimator of a parameter is a formula using the random sample. Since the moments of a PDF will generally be a function of the parameters, the chapter also focuses on estimators of the first two moments. It begins by computing the theoretical mean and theoretical variance of the sample mean. The chapter helps the reader to learn how these random variables are related to the mean and variance of the sampling density. It discusses method of moments, sufficient statistics and data compression, and Bayesian parameter estimation. The frequentist (non-Bayesian) statistician usually chooses the maximum likelihood estimator, which maximizes the likelihood function.

Cited by 0

Parameter estimation

David Weinberg, The Promise, Bayesian Inference Astrophysics et al. · 2016

Cited by 0

PARAMETER ESTIMATION

Prof Dr. Mübeccel Demirekler, Prof Dr. Kemal Leblebicioğlu, Prof Dr. Mübeccel Demirekler et al. · 2009

Date: I hereby declare that all information in this document has been obtained and presented in accordance with academic rules and ethical conduct. I also declare that, as required by these rules and conduct, I have fully cited and referenced all material and results that are not original to this work.

Cited by 0

Parameter estimation.

Fillipe Georgiou (10643390), Camille Buhl (145762), J. E. F. Green (3189057) et al. · Figshare · 2025

Derivation of parameters given in the simulation section. (PDF)

Cited by 0Open access

Parameter estimation.

Fengdi Zhao (19321691), Xin Ma (37268), Bing Yao (336395) et al. · Figshare · 2024

<p>Using the same simulation strategy in Scenario 4 (log2FC = 2.5), we assume 20% differential peaks and simulate the read counts based on ZINB for 4000 peaks across 100 cells in each group. <b>A</b>. True dispersion is plotted against the estimated dispersion from ZINB(<i>μ</i>, <i>ϕ</i>, <i>p</i>) and scaDA on a log scale plot. <b>B</b>. MSE calculated between true and estimated mean, dispersion, and prevalence from ZINB(<i>μ</i>, <i>ϕ</i>, <i>p</i>) and scaDA.</p>

Cited by 0Open access

Parameter Estimation

Ferdinand van der Heijden, Robert P. W. Duin, D. de Ridder et al. · 2004

This chapter contains sections titled: Bayesian estimation Performance of estimators Data fitting Overview of the family of estimators Selected bibliography Exercises

Cited by 0

Parameter estimation.

Daniela S Santurio (21030812), Luciana R C Barros (21030815), Ingmar Glauche (261610) et al. · Figshare · 2025

Model parameters, their biological meanings, bounds, and references used for parameter estimation. (TIF)

Cited by 0Open access

Parameter estimation.

Punya Alahakoon (17458435), Peter G. Taylor (1978135), James M. McCaw (8275374) · Figshare · 2023

<p>Supporting information containing the details of parameter estimation.</p> <p>(PDF)</p>

Cited by 0Open access

Parameter Estimation

Robert Ross · 2019

This chapter discusses techniques to estimate parameters and their accuracies. It studies two types of parameter estimation: maximum likelihood (ML) and linear regression (LR). Their accuracy and how they relate to graphical analysis are included. The relation of parameter estimation to graphical analysis is elaborated for the Weibull distribution, the exponential distribution, and the normal distribution. The chapter discusses the general aspects of parameter estimation and some characteristics of estimators in greater depth, with additional focus on the asymptotic behaviour of estimated parameters and their moments. The ML estimator is explained for both the uncensored case and the censored case. The LR estimator works with ranked plotting positions quite similar to graphical analysis with parametric plots. The chapter discusses the adjusted ranking method and the adjusted plotting position method for manipulating the ranked plotting positions.

Cited by 0

Parameter Estimation

Lee G. Cooper, Masao Nakanishi · International series in quantitative marketing · 1988

Cited by 1