Relevant bibliographies by topics / Bayesian estimation

Contents

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

Academic literature on the topic 'Bayesian estimation'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Bayesian estimation"

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Eldemery, E. M., A. M. Abd-Elfattah, K. M. Mahfouz, and Mohammed M. El Genidy. "Bayesian and E-Bayesian Estimation for the Generalized Rayleigh Distribution under Different Forms of Loss Functions with Real Data Application." Journal of Mathematics 2023 (August 31, 2023): 1–25. http://dx.doi.org/10.1155/2023/5454851.

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This paper investigates the estimation of an unknown shape parameter of the generalized Rayleigh distribution using Bayesian and expected Bayesian estimation techniques based on type-II censoring data. Subsequently, these estimators are obtained using four different loss functions: the linear exponential loss function, the weighted linear exponential loss function, the compound linear exponential loss function, and the weighted compound linear exponential loss function. The weighted compound linear exponential loss function is a novel suggested loss function generated by combining weights with
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admin, admin. "On The Bayesian Estimation of Parameters of SQDM." Neutrosophic and Information Fusion 3, no. 1 (2024): 34–41. http://dx.doi.org/10.54216/nif.030105.

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This work is concerned with the problem of estimating parameters of spatial quadratic models by Bayesian technique (SQDM). This technique involves the prior information of the first and second moment of the parameters, where its estimation model is called the Bayesian quadratic unbiased estimator. The results of the estimation are taken in compared with the estimates of minimum norm quadratic unbiased estimators.
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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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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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Xiang, Ning, and Christopher Landschoot. "Bayesian Inference for Acoustic Direction of Arrival Analysis Using Spherical Harmonics." Entropy 21, no. 6 (2019): 579. http://dx.doi.org/10.3390/e21060579.

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This work applies two levels of inference within a Bayesian framework to accomplish estimation of the directions of arrivals (DoAs) of sound sources. The sensing modality is a spherical microphone array based on spherical harmonics beamforming. When estimating the DoA, the acoustic signals may potentially contain one or multiple simultaneous sources. Using two levels of Bayesian inference, this work begins by estimating the correct number of sources via the higher level of inference, Bayesian model selection. It is followed by estimating the directional information of each source via the lower
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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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Shadmehr, Reza, and David Z. D'Argenio. "A Neural Network for Nonlinear Bayesian Estimation in Drug Therapy." Neural Computation 2, no. 2 (1990): 216–25. http://dx.doi.org/10.1162/neco.1990.2.2.216.

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The feasibility of developing a neural network to perform nonlinear Bayesian estimation from sparse data is explored using an example from clinical pharmacology. The problem involves estimating parameters of a dynamic model describing the pharmacokinetics of the bronchodilator theophylline from limited plasma concentration measurements of the drug obtained in a patient. The estimation performance of a backpropagation trained network is compared to that of the maximum likelihood estimator as well as the maximum a posteriori probability estimator. In the example considered, the estimator predict
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Wang, Wei, and Wenhao Gui. "Estimation and Bayesian Prediction for the Generalized Exponential Distribution Under Type-II Censoring." Symmetry 17, no. 2 (2025): 222. https://doi.org/10.3390/sym17020222.

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This research focuses on the prediction and estimation problems for the generalized exponential distribution under Type-II censoring. Firstly, maximum likelihood estimations for the parameters of the generalized exponential distribution are computed using the EM algorithm. Additionally, confidence intervals derived from the Fisher information matrix are developed and analyzed alongside two bootstrap confidence intervals for comparison. Compared to classical maximum likelihood estimation, Bayesian inference proves to be highly effective in handling censored data. This study explores Bayesian in
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Itagaki, Hiroshi, Hiroo Asada, and Seiichi Itoh. "Bayesian Estimation." Journal of the Society of Naval Architects of Japan 1985, no. 157 (1985): 285–94. http://dx.doi.org/10.2534/jjasnaoe1968.1985.285.

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Liu, Kaiwei, and Yuxuan Zhang. "The E-Bayesian Estimation for Lomax Distribution Based on Generalized Type-I Hybrid Censoring Scheme." Mathematical Problems in Engineering 2021 (May 19, 2021): 1–19. http://dx.doi.org/10.1155/2021/5570320.

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This article studies the E-Bayesian estimation of the unknown parameter of Lomax distribution based on generalized Type-I hybrid censoring. Under square error loss and LINEX loss functions, we get the E-Bayesian estimation and compare its effectiveness with Bayesian estimation. To measure the error of E-Bayesian estimation, the expectation of mean square error (E-MSE) is introduced. With Markov chain Monte Carlo technology, E-Bayesian estimations are computed. Metropolis–Hastings algorithm is applied within the process. Similarly, the credible interval for the parameter is calculated. Then, we
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Dissertations / Theses on the topic "Bayesian estimation"

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Rademeyer, Estian. "Bayesian kernel density estimation." Diss., University of Pretoria, 2017. http://hdl.handle.net/2263/64692.

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This dissertation investigates the performance of two-class classi cation credit scoring data sets with low default ratios. The standard two-class parametric Gaussian and naive Bayes (NB), as well as the non-parametric Parzen classi ers are extended, using Bayes' rule, to include either a class imbalance or a Bernoulli prior. This is done with the aim of addressing the low default probability problem. Furthermore, the performance of Parzen classi cation with Silverman and Minimum Leave-one-out Entropy (MLE) Gaussian kernel bandwidth estimation is also investigated. It is shown that the n
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Weiss, Yair. "Bayesian motion estimation and segmentation." Thesis, Massachusetts Institute of Technology, 1998. http://hdl.handle.net/1721.1/9354.

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Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 1998.<br>Includes bibliographical references (leaves 195-204).<br>Estimating motion in scenes containing multiple moving objects remains a difficult problem in computer vision yet is solved effortlessly by humans. In this thesis we present a computational investigation of this astonishing performance in human vision. The method we use throughout is to formulate a small number of assumptions and see the extent to which the optimal interpretation given these assumptions corresponds to the human percept.
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Bouda, Milan. "Bayesian Estimation of DSGE Models." Doctoral thesis, Vysoká škola ekonomická v Praze, 2012. http://www.nusl.cz/ntk/nusl-200007.

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Thesis is dedicated to Bayesian Estimation of DSGE Models. Firstly, the history of DSGE modeling is outlined as well as development of this macroeconometric field in the Czech Republic and in the rest of the world. Secondly, the comprehensive DSGE framework is described in detail. It means that everyone is able to specify or estimate arbitrary DSGE model according to this framework. Thesis contains two empirical studies. The first study describes derivation of the New Keynesian DSGE Model and its estimation using Bayesian techniques. This model is estimated with three different Taylor rules an
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Pramanik, Santanu. "The Bayesian and approximate Bayesian methods in small area estimation." College Park, Md.: University of Maryland, 2008. http://hdl.handle.net/1903/8856.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2008.<br>Thesis research directed by: Joint Program in Survey Methodology. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Campolieti, Michele. "Bayesian estimation of discrete duration models." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape16/PQDD_0001/NQ27884.pdf.

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Hissmann, Michael. "Bayesian estimation for white light interferometry." Berlin Pro Business, 2005. http://shop.pro-business.com/product_info.php?products_id=357.

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Makarava, Natallia. "Bayesian estimation of self-similarity exponent." Phd thesis, Universität Potsdam, 2012. http://opus.kobv.de/ubp/volltexte/2013/6409/.

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Estimation of the self-similarity exponent has attracted growing interest in recent decades and became a research subject in various fields and disciplines. Real-world data exhibiting self-similar behavior and/or parametrized by self-similarity exponent (in particular Hurst exponent) have been collected in different fields ranging from finance and human sciencies to hydrologic and traffic networks. Such rich classes of possible applications obligates researchers to investigate qualitatively new methods for estimation of the self-similarity exponent as well as identification of long-range d
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Graham, Matthew Corwin 1986. "Robust Bayesian state estimation and mapping." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/98678.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2015.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 135-146).<br>Virtually all robotic and autonomous systems rely on navigation and mapping algorithms (e.g. the Kalman filter or simultaneous localization and mapping (SLAM)) to determine their location in the world. Unfortunately, these algorithms are not robust to outliers and even a single faulty measurement can cause a catastrophic failure of the navigation system. This thesis proposes several novel rob
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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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Xing, Guan. "LASSOING MIXTURES AND BAYESIAN ROBUST ESTIMATION." Case Western Reserve University School of Graduate Studies / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=case1164135815.

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Books on the topic "Bayesian estimation"

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Haug, Anton J. Bayesian Estimation and Tracking. John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118287798.

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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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Campolieti, Michele. Bayesian estimation of discrete duration models. National Library of Canada = Bibliothèque nationale du Canada, 1997.

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Haug, Anton J. Bayesian estimation and tracking: A practical guide. Wiley, 2012.

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Blom, H. A. P. Bayesian estimation for decision directed stochastic control. National Aerospace Laboratory, 1990.

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Duo, Qin. Has Bayesian estimation principle ever used Bayes' rule? London University, Queen Mary and Westfield College, Department of Economics, 1994.

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Savchuk, V. P. Bayesian methods for statistical estimation with application to reliability. World Federation Publishers, 1996.

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Guttman, Irwin. Bayesian estimation in two-way tables with heterogeneous variances. University of Toronto, Dept. of Statistics, 1987.

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Book chapters on the topic "Bayesian estimation"

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Zwanzig, Silvelyn, and Rauf Ahmad. "Estimation." In Bayesian Inference. Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003221623-7.

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Shekhar, Shashi, and Hui Xiong. "Bayesian Estimation." In Encyclopedia of GIS. Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_92.

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Salsburg, David S. "Bayesian Estimation." In The Use of Restricted Significance Tests in Clinical Trials. Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4612-4414-1_12.

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Keener, Robert W. "Bayesian Estimation." In Theoretical Statistics. Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-93839-4_7.

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Chaudhuri, Subhasis, and Ketan Kotwal. "Bayesian Estimation." In Hyperspectral Image Fusion. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-7470-8_5.

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Heitzinger, Clemens. "Bayesian Estimation." In Algorithms with JULIA. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-16560-3_14.

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Cohen, Shay. "Bayesian Estimation." In Synthesis Lectures on Human Language Technologies. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-031-02161-9_4.

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Johnson, Matthew S., and Sandip Sinharay. "Bayesian Estimation." In Handbook of Item Response Theory. Chapman and Hall/CRC, 2017. http://dx.doi.org/10.1201/b19166-13.

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Mills, Jeffrey A., and Olivier Parent. "Bayesian MCMC Estimation." In Handbook of Regional Science. Springer Berlin Heidelberg, 2019. http://dx.doi.org/10.1007/978-3-642-36203-3_89-1.

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Robert, Christian P. "Bayesian Point Estimation." In Springer Texts in Statistics. Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4757-4314-2_4.

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Conference papers on the topic "Bayesian estimation"

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Iseki, Toshio. "A Study on Akaike’s Bayesian Information Criterion in Wave Estimation." In ASME 2011 30th International Conference on Ocean, Offshore and Arctic Engineering. ASMEDC, 2011. http://dx.doi.org/10.1115/omae2011-49170.

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A feasibility study of Bayesian wave estimation was carried out to investigate the relationship between the minimum Akaike’s Bayesian information criterion (ABIC) and the estimated wave parameters. The ship response functions, which were used for the Bayesian wave estimation together with the ship motion cross spectra, were simply modified and compared with the normal response functions in connection with the accuracy of estimated wave parameters. Moreover, the concept of the ABIC surfaces was introduced to investigate the optimum estimates from the stochastic viewpoint and the physical viewpo
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Fischer, R. "Bayesian background estimation." In The 19th international workshop on bayesium inference and maximum entropy methods in science and engineering. AIP, 2001. http://dx.doi.org/10.1063/1.1381857.

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Picci, Giorgio, and Bin Zhu. "Bayesian Frequency Estimation." In 2019 18th European Control Conference (ECC). IEEE, 2019. http://dx.doi.org/10.23919/ecc.2019.8796054.

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Elvira, Clement, Pierre Chainais, and Nicolas Dobigeon. "Bayesian nonparametric subspace estimation." In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2017. http://dx.doi.org/10.1109/icassp.2017.7952556.

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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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Bridle, S. L., J. P. Kneib, S. Bardeau, and S. F. Gull. "BAYESIAN GALAXY SHAPE ESTIMATION." In Proceedings of the Yale Cosmology Workshop. WORLD SCIENTIFIC, 2002. http://dx.doi.org/10.1142/9789812778017_0006.

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Chen, Chulong, and Michael D. Zoltowski. "Bayesian sparse channel estimation." In SPIE Defense, Security, and Sensing. SPIE, 2012. http://dx.doi.org/10.1117/12.919302.

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Ninness, Brett, Khoa T. Tran, and Christopher M. Kellett. "Bayesian dynamic system estimation." In 2014 IEEE 53rd Annual Conference on Decision and Control (CDC). IEEE, 2014. http://dx.doi.org/10.1109/cdc.2014.7039656.

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Papageorgiou, Ioannis, and Ioannis Kontoyiannis. "Truly Bayesian Entropy Estimation." In 2023 IEEE Information Theory Workshop (ITW). IEEE, 2023. http://dx.doi.org/10.1109/itw55543.2023.10161645.

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Cuevas, Alejandro, Sebastian Lopez, Danilo Mandic, and Felipe Tobar. "Bayesian autoregressive spectral estimation." In 2021 IEEE Latin American Conference on Computational Intelligence (LA-CCI). IEEE, 2021. http://dx.doi.org/10.1109/la-cci48322.2021.9769834.

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Reports on the topic "Bayesian estimation"

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Gray, Kathy, Robert Keane, Ryan Karpisz, Alyssa Pedersen, Rick Brown, and Taylor Russell. Bayesian techniques for surface fuel loading estimation. U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, 2016. http://dx.doi.org/10.2737/rmrs-rn-74.

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Gray, Kathy, Robert Keane, Ryan Karpisz, Alyssa Pedersen, Rick Brown, and Taylor Russell. Bayesian techniques for surface fuel loading estimation. U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, 2016. http://dx.doi.org/10.2737/rmrs-rn-74.

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Tang, Victor K., Ronald B. Sindler, and Raymond M. Shirven. Bayesian Estimation of n in a Binomial Distribution. Defense Technical Information Center, 1987. http://dx.doi.org/10.21236/ada196623.

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Quijano, Jorge E., Stan E. Dosso, Jan Dettmer, Lisa M. Zurk, and Martin Siderius. Bayesian Ambient Noise Inversion for Geoacoustic Uncertainty Estimation. Defense Technical Information Center, 2011. http://dx.doi.org/10.21236/ada571872.

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Quijano, Jorge E., Stan E. Dosso, Jan Dettmer, Lisa M. Zurk, and Martin Siderius. Bayesian Ambient Noise Inversion for Geoacoustic Uncertainty Estimation. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada575020.

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Fraley, Chris, and Adrian E. Raftery. Bayesian Regularization for Normal Mixture Estimation and Model-Based Clustering. Defense Technical Information Center, 2005. http://dx.doi.org/10.21236/ada454825.

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Rodríguez-Niño, Norberto. Bayesian model estimation and selection for the weekly colombian exchange rate. Banco de la República, 2000. http://dx.doi.org/10.32468/be.161.

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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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Arias, Jonas, Jesús Fernández-Villaverde, Juan Rubio Ramírez, and Minchul Shin. Bayesian Estimation of Epidemiological Models: Methods, Causality, and Policy Trade-Offs. National Bureau of Economic Research, 2021. http://dx.doi.org/10.3386/w28617.

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Ness, Troy. Bayesian Estimation of Nonparametric Failure Time Distribution for Arbitrary Censored Data. Iowa State University, 2022. http://dx.doi.org/10.31274/cc-20240624-282.

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