Relevant bibliographies by topics / Bayesian parameter estimation

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Bayesian parameter estimation'

Author: Grafiati
Published: 4 June 2021
Last updated: 26 July 2025

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Journal articles on the topic "Bayesian parameter estimation"

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Kramer, S. C., and H. W. Sorenson. "Bayesian parameter estimation." IEEE Transactions on Automatic Control 33, no. 2 (1988): 217–22. http://dx.doi.org/10.1109/9.395.

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Mudasir, Sofi, Ajaz Bhat, Sheikh Ahmad, et al. "A dual approach to parameter estimation classical vs. Bayesian methods in power Rayleigh modelling." Thermal Science 28, no. 6 Part B (2024): 4877–94. https://doi.org/10.2298/tsci2406877m.

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In this article, we investigated the problem of estimating the parameters of power Rayleigh distribution using a range of classical and Bayesian estimate strategies. For applied statisticians and reliability engineers, parameter estimation provides a guide for choosing the best method of estimating the model parameters. Six frequentist estimation methods, including maximum likelihood estimation, Cramer-von Mises estimation, Anderson-Darling estimation, least square estimation, weighted least square estimation, and maximum product of spacing estimation, were taken into consideration when estima
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Li, Mingyao, and Juanping Zhu. "Bayesian Adaptive Estimation with Theoretical Bound: An Exploration-Exploitation Approach." Computational Intelligence and Neuroscience 2022 (December 12, 2022): 1–9. http://dx.doi.org/10.1155/2022/1143056.

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This paper investigates the theoretical bound to reduce the parameter uncertainty in Bayesian adaptive estimation for psychometric functions and proposes an exploration-exploitation (E-E) approach to improve the computation efficiency for parameter estimations. When the experimental trial goes on, the uncertainty of the parameters decreases dramatically and the space between the maximal mutual information and the theoretical bound gets narrower, so the advantage of classical Bayesian adaptive estimation algorithm diminishes. This approach tries to trade off the exploration (parameter posterior
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Mohammed Alomari, Huda. "Bayes Estimations for Parameter of the Poisson distribution with Progressive Schemes." Academic Journal of Applied Mathematical Sciences, no. 102 (October 9, 2024): 14–23. https://doi.org/10.32861/ajams.10.2.14.23.

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This study introduces maximum likelihood and Bayesian approaches to Poisson parameter estimation using posterior distribution. I discuss three types of loss functions: the asymmetric linear exponential loss function, non-linear exponential loss function, and squared error loss function. Their performance is compared with the maximum likelihood estimator using mean squared error (MSE) as the test criterion. The proposed method with the classical estimator (maximum likelihood estimator) is better than that with the non-classical estimators for point estimation with different sample sizes. Maximu
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Al-Bossly, Afrah. "E-Bayesian and Bayesian Estimation for the Lomax Distribution under Weighted Composite LINEX Loss Function." Computational Intelligence and Neuroscience 2021 (December 11, 2021): 1–10. http://dx.doi.org/10.1155/2021/2101972.

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The main contribution of this work is the development of a compound LINEX loss function (CLLF) to estimate the shape parameter of the Lomax distribution (LD). The weights are merged into the CLLF to generate a new loss function called the weighted compound LINEX loss function (WCLLF). Then, the WCLLF is used to estimate the LD shape parameter through Bayesian and expected Bayesian (E-Bayesian) estimation. Subsequently, we discuss six different types of loss functions, including square error loss function (SELF), LINEX loss function (LLF), asymmetric loss function (ASLF), entropy loss function
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Gao, Huiqing, Zhanshou Chen, and Fuxiao Li. "Linear Bayesian Estimation of Misrecorded Poisson Distribution." Entropy 26, no. 1 (2024): 62. http://dx.doi.org/10.3390/e26010062.

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Parameter estimation is an important component of statistical inference, and how to improve the accuracy of parameter estimation is a key issue in research. This paper proposes a linear Bayesian estimation for estimating parameters in a misrecorded Poisson distribution. The linear Bayesian estimation method not only adopts prior information but also avoids the cumbersome calculation of posterior expectations. On the premise of ensuring the accuracy and stability of computational results, we derived the explicit solution of the linear Bayesian estimation. Its superiority was verified through nu
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Guure, Chris Bambey, Noor Akma Ibrahim, and Al Omari Mohammed Ahmed. "Bayesian Estimation of Two-Parameter Weibull Distribution Using Extension of Jeffreys' Prior Information with Three Loss Functions." Mathematical Problems in Engineering 2012 (2012): 1–13. http://dx.doi.org/10.1155/2012/589640.

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The Weibull distribution has been observed as one of the most useful distribution, for modelling and analysing lifetime data in engineering, biology, and others. Studies have been done vigorously in the literature to determine the best method in estimating its parameters. Recently, much attention has been given to the Bayesian estimation approach for parameters estimation which is in contention with other estimation methods. In this paper, we examine the performance of maximum likelihood estimator and Bayesian estimator using extension of Jeffreys prior information with three loss functions, n
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Wijayanti, Rina. "PENAKSIRAN PARAMETER ANALISIS REGRESI COX DAN ANALISIS SURVIVAL BAYESIAN." PRISMATIKA: Jurnal Pendidikan dan Riset Matematika 1, no. 2 (2019): 16–26. http://dx.doi.org/10.33503/prismatika.v1i2.427.

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In the theory of estimation, there are two approaches, namely the classical statistical approach and global statistical approach (Bayesian). Classical statistics are statistics in which the procedure is the decision based only on the data samples taken from the population. While Bayesian statistics in making decisions based on new information from the observed data (sample) and prior knowledge. At this writing Cox Regression Analysis will be taken as an example of parameter estimation by the classical statistical approach Survival Analysis and Bayesian statistical approach as an example of glo
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Supharakonsakun, Yadpirun. "Bayesian Approaches for Poisson Distribution Parameter Estimation." Emerging Science Journal 5, no. 5 (2021): 755–74. http://dx.doi.org/10.28991/esj-2021-01310.

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The Bayesian approach, a non-classical estimation technique, is very widely used in statistical inference for real world situations. The parameter is considered to be a random variable, and knowledge of the prior distribution is used to update the parameter estimation. Herein, two Bayesian approaches for Poisson parameter estimation by deriving the posterior distribution under the squared error loss or quadratic loss functions are proposed. Their performances were compared with frequentist (maximum likelihood estimator) and Empirical Bayes approaches through Monte Carlo simulations. The mean s
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Alduais, Fuad. "Comparison of classical and Bayesian estimators to estimate the parameters in Weibull distribution under weighted general entropy loss function." International Journal of ADVANCED AND APPLIED SCIENCES 8, no. 3 (2021): 57–62. http://dx.doi.org/10.21833/ijaas.2021.03.008.

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In this work, we have developed a General Entropy loss function (GE) to estimate parameters of Weibull distribution (WD) based on complete data when both shape and scale parameters are unknown. The development is done by merging weight into GE to produce a new loss function called the weighted General Entropy loss function (WGE). Then, we utilized WGE to derive the parameters of the WD. After, we compared the performance of the developed estimation in this work with the Bayesian estimator using the GE loss function. Bayesian estimator using square error (SE) loss function, Ordinary Least Squar
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Dissertations / Theses on the topic "Bayesian parameter estimation"

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Vega-Brown, Will (William Robert). "Predictive parameter estimation for Bayesian filtering." Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/81715.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2013.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 113-117).<br>In this thesis, I develop CELLO, an algorithm for predicting the covariances of any Gaussian model used to account for uncertainty in a complex system. The primary motivation for this work is state estimation; often, complex raw sensor measurements are processed into low dimensional observations of a vehicle state. I argue that the covariance of these observations can be well-modelled as a function of the r
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White, Staci A. "Quantifying Model Error in Bayesian Parameter Estimation." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1433771825.

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Callahan, Margaret D. "Bayesian Parameter Estimation and Inference Across Scales." Case Western Reserve University School of Graduate Studies / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=case1459523006.

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Sonogashira, Motoharu. "Variational Bayesian Image Restoration with Transformation Parameter Estimation." Kyoto University, 2018. http://hdl.handle.net/2433/232409.

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Torrence, Robert Billington. "Bayesian Parameter Estimation on Three Models of Influenza." Thesis, Virginia Tech, 2017. http://hdl.handle.net/10919/77611.

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Mathematical models of viral infections have been informing virology research for years. Estimating parameter values for these models can lead to understanding of biological values. This has been successful in HIV modeling for the estimation of values such as the lifetime of infected CD8 T-Cells. However, estimating these values is notoriously difficult, especially for highly complex models. We use Bayesian inference and Monte Carlo Markov Chain methods to estimate the underlying densities of the parameters (assumed to be continuous random variables) for three models of influenza. We discuss t
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Reed, Craig. "Bayesian parameter estimation and variable selection for quantile regression." Thesis, Brunel University, 2011. http://bura.brunel.ac.uk/handle/2438/6118.

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The principal goal of this work is to provide efficient algorithms for implementing the Bayesian approach to quantile regression. There are two major obstacles to overcome in order to achieve this. Firstly, it is necessary to specify a suitable likelihood given that the frequentist approach generally avoids such speci cations. Secondly, sampling methods are usually required as analytical expressions for posterior summaries are generally unavailable in closed form regardless of the prior used. The asymmetric Laplace (AL) likelihood is a popular choice and has a direct link to the frequentist pr
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Keim, Michelle. "Bayesian information retrieval /." Thesis, Connect to this title online; UW restricted, 1997. http://hdl.handle.net/1773/8937.

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Cooper, Matthew. "Bayesian system identification for nonlinear dynamical vehicle models." Thesis, Queensland University of Technology, 2021. https://eprints.qut.edu.au/213212/1/Matthew_Cooper_Thesis.pdf.

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This thesis investigates the use of novel Bayesian system identification techniques to estimate unknown parameters in nonlinear vehicle dynamics. In the first part of this thesis, a dual merging particle filter is proposed that accurately estimates non-Gaussian posterior parameter distributions for different vehicle models. In the second part of this thesis, a novel myopic sequential technique is proposed to design informative experiments for estimating the unknown parameters of a real-world robotic vehicle. This myopic technique is extended in the last part of the thesis to incorporate a roll
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May, Thomas Joseph. "Minimally Corrective, Approximately Recovering Priors to Correct Expert Judgement in Bayesian Parameter Estimation." Thesis, Virginia Tech, 2015. http://hdl.handle.net/10919/54593.

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Bayesian parameter estimation is a popular method to address inverse problems. However, since prior distributions are chosen based on expert judgement, the method can inherently introduce bias into the understanding of the parameters. This can be especially relevant in the case of distributed parameters where it is difficult to check for error. To minimize this bias, we develop the idea of a minimally corrective, approximately recovering prior (MCAR prior) that generates a guide for the prior and corrects the expert supplied prior according to that guide. We demonstrate this approach for the 1
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Ozkan, Emre. "Particle Methods For Bayesian Multi-object Tracking And Parameter Estimation." Phd thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/3/12610986/index.pdf.

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In this thesis a number of improvements have been established for specific methods which utilize sequential Monte Carlo (SMC), aka. Particle filtering (PF) techniques. The first problem is the Bayesian multi-target tracking (MTT) problem for which we propose the use of non-parametric Bayesian models that are based on time varying extension of Dirichlet process (DP) models. The second problem studied in this thesis is an important application area for the proposed DP based MTT method<br>the tracking of vocal tract resonance frequencies of the speech signals. Lastly, we investigate SMC based par
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Books on the topic "Bayesian parameter estimation"

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Bretthorst, G. Larry. Bayesian Spectrum Analysis and Parameter Estimation. Springer New York, 1988. http://dx.doi.org/10.1007/978-1-4684-9399-3.

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Harney, Hanns L. Bayesian inference: Parameter estimation and decisions. Springer, 2002.

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Harney, Hanns L. Bayesian Inference: Parameter Estimation and Decisions. Springer Berlin Heidelberg, 2003.

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Saleh, A. K. Md. Ehsanes. Theory of Preliminary Test and Stein-Type Estimation with Applications. John Wiley & Sons, Ltd., 2006.

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Kurt, Hoffmann. Improved estimation of distribution parameters: Stein-type estimators. B.G. Teubner, 1992.

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Bretthorst, G. Larry. Bayesian Spectrum Analysis and Parameter Estimation. Springer London, Limited, 2013.

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Bayesian spectrum analysis and parameter estimation. Springer-Verlag, 1988.

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Bretthorst, G. Larry. Bayesian Spectrum Analysis and Parameter Estimation. Springer, 2013.

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Bayesian inference: Parameter estimation and decisions. Springer, 2003.

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Kruschke, John K., and Wolf Vanpaemel. Bayesian Estimation in Hierarchical Models. Edited by Jerome R. Busemeyer, Zheng Wang, James T. Townsend, and Ami Eidels. Oxford University Press, 2015. http://dx.doi.org/10.1093/oxfordhb/9780199957996.013.13.

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Bayesian data analysis involves describing data by meaningful mathematical models, and allocating credibility to parameter values that are consistent with the data and with prior knowledge. The Bayesian approach is ideally suited for constructing hierarchical models, which are useful for data structures with multiple levels, such as data from individuals who are members of groups which in turn are in higher-level organizations. Hierarchical models have parameters that meaningfully describe the data at their multiple levels and connect information within and across levels. Bayesian methods are
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Book chapters on the topic "Bayesian parameter estimation"

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Simoen, E., and G. Lombaert. "Bayesian Parameter Estimation." In Identification Methods for Structural Health Monitoring. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-32077-9_4.

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Bretthorst, G. Larry. "Spectral Estimation." In Bayesian Spectrum Analysis and Parameter Estimation. Springer New York, 1988. http://dx.doi.org/10.1007/978-1-4684-9399-3_6.

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Ching, Jianye. "Bayesian parameter estimation and prediction." In Bayesian Machine Learning in Geotechnical Site Characterization. CRC Press, 2024. http://dx.doi.org/10.1201/9781003309765-3.

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Dose, V., and W. Von Der Linden. "Outlier Tolerant Parameter Estimation." In Maximum Entropy and Bayesian Methods Garching, Germany 1998. Springer Netherlands, 1999. http://dx.doi.org/10.1007/978-94-011-4710-1_4.

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Bretthorst, G. Larry. "Introduction." In Bayesian Spectrum Analysis and Parameter Estimation. Springer New York, 1988. http://dx.doi.org/10.1007/978-1-4684-9399-3_1.

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Bretthorst, G. Larry. "Single Stationary Sinusoid Plus Noise." In Bayesian Spectrum Analysis and Parameter Estimation. Springer New York, 1988. http://dx.doi.org/10.1007/978-1-4684-9399-3_2.

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Bretthorst, G. Larry. "The General Model Equation Plus Noise." In Bayesian Spectrum Analysis and Parameter Estimation. Springer New York, 1988. http://dx.doi.org/10.1007/978-1-4684-9399-3_3.

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Bretthorst, G. Larry. "Estimating the Parameters." In Bayesian Spectrum Analysis and Parameter Estimation. Springer New York, 1988. http://dx.doi.org/10.1007/978-1-4684-9399-3_4.

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Bretthorst, G. Larry. "Model Selection." In Bayesian Spectrum Analysis and Parameter Estimation. Springer New York, 1988. http://dx.doi.org/10.1007/978-1-4684-9399-3_5.

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Bretthorst, G. Larry. "Applications." In Bayesian Spectrum Analysis and Parameter Estimation. Springer New York, 1988. http://dx.doi.org/10.1007/978-1-4684-9399-3_7.

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Conference papers on the topic "Bayesian parameter estimation"

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Tan, Bendong, Junbo Zhao, and Nan Duan. "Amortized Bayesian Parameter Estimation Approach for WECC Composite Load Model." In 2024 IEEE Power & Energy Society General Meeting (PESGM). IEEE, 2024. http://dx.doi.org/10.1109/pesgm51994.2024.10689232.

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Sagiv, Shay, Hagit Messer, Hai Victor Habi, and Joseph Tabrikian. "Lower Bounds on Non-Bayesian Parameter Estimation Errors Under Reparameterization." In 2024 IEEE 13th Sensor Array and Multichannel Signal Processing Workshop (SAM). IEEE, 2024. http://dx.doi.org/10.1109/sam60225.2024.10636614.

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Hoballah, I., and P. Varshney. "Distributed Bayesian parameter estimation." In 26th IEEE Conference on Decision and Control. IEEE, 1987. http://dx.doi.org/10.1109/cdc.1987.272937.

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Liu, Z. Y., Y. Wang, W. P. Wang, Z. Z. Ji, and W. Y. Lu. "Bayesian Parameter Estimation in LDA." In International Conference on Computer Information Systems and Industrial Applications. Atlantis Press, 2015. http://dx.doi.org/10.2991/cisia-15.2015.225.

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Dichandra, D., I. Fithriani, and S. Nurrohmah. "Parameter estimation of Bayesian quantile regression." In PROCEEDINGS OF THE 6TH INTERNATIONAL SYMPOSIUM ON CURRENT PROGRESS IN MATHEMATICS AND SCIENCES 2020 (ISCPMS 2020). AIP Publishing, 2021. http://dx.doi.org/10.1063/5.0059103.

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Wendt, Herwig, Nicolas Dobigeon, Jean-Yves Tourneret, and Patrice Abry. "Bayesian estimation for the multifractality parameter." In ICASSP 2013 - 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2013. http://dx.doi.org/10.1109/icassp.2013.6638929.

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Balytskyi, Yaroslav, Manohar Raavi, and Sang-Yoon Chang. "P T-Enhanced Bayesian Parameter Estimation." In 2021 IEEE International Conference on Quantum Computing and Engineering (QCE). IEEE, 2021. http://dx.doi.org/10.1109/qce52317.2021.00022.

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Fu, Chang, Zhe Yu, Di Shi, et al. "Bayesian Estimation Based Parameter Estimation for Composite Load." In 2019 IEEE Power & Energy Society General Meeting (PESGM). IEEE, 2019. http://dx.doi.org/10.1109/pesgm40551.2019.8974094.

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Fackler, Cameron, Eric Dieckman, and Ning Xiang. "Porous material parameter estimation: A Bayesian approach." In BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING: 31st International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering. AIP, 2012. http://dx.doi.org/10.1063/1.3703649.

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Nitzan, Eyal, Joseph Tabrikian, and Tirza Routtenberg. "Bayesian cyclic bounds for periodic parameter estimation." In 2013 IEEE 5th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP). IEEE, 2013. http://dx.doi.org/10.1109/camsap.2013.6714069.

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Reports on the topic "Bayesian parameter estimation"

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Tsutakawa, Robert K., and Jane Johnson. Bayesian Ability Estimation via 3PL (Three-Parameter Logistic) with Partially Known Item Parameters. Defense Technical Information Center, 1988. http://dx.doi.org/10.21236/ada198207.

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Crews, John H., and Ralph C. Smith. Modeling and Bayesian Parameter Estimation for Shape Memory Alloy Bending Actuators. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada556967.

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Baltagi, Badi H., Georges Bresson, Anoop Chaturvedi та Guy Lacroix. Robust dynamic space-time panel data models using ε-contamination: An application to crop yields and climate change. CIRANO, 2023. http://dx.doi.org/10.54932/ufyn4045.

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This paper extends the Baltagi et al. (2018, 2021) static and dynamic ε-contamination papers to dynamic space-time models. We investigate the robustness of Bayesian panel data models to possible misspecification of the prior distribution. The proposed robust Bayesian approach departs from the standard Bayesian framework in two ways. First, we consider the ε-contamination class of prior distributions for the model parameters as well as for the individual effects. Second, both the base elicited priors and the ε-contamination priors use Zellner (1986)’s g-priors for the variance-covariance matric
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BARUMERLI, Roberto, Piotr MAJDAK, Michele GERONAZZO, David MEIJER, Federico AVANZINI, and Robert BAUMGARTNER. Evaluation of spatial tasks in virtual acoustic environments by means of modeling individual localization performances. Verlag der Österreichischen Akademie der Wissenschaften, 2025. https://doi.org/10.1553/ica_2022_acoustic-environments.

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Virtual acoustic environments (VAEs) are an excellent tool in hearing research, especially in the context of investigating spatial-hearing abilities. On the one hand, the development of VAEs requires a solid evaluation, which can be simplified by applying auditory models. On the other hand, VAE research provides data, which can support the further improvement of auditory models. Here, we describe how Bayesian inference can predict listeners' behavior when estimating the spatial direction of a static sound source presented in a VAE experiment. We show which components of the behavioral process
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