Relevant bibliographies by topics / Bayesian estimation

Academic literature on the topic 'Bayesian estimation'

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Published: 4 June 2021
Last updated: 4 May 2024

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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 the compound linear exponential loss function. We use the gamma distribution as a prior distribution. In addition, the expected Bayesian estimator is obtained through three different prior distributions of the hyperparameters. Moreover, depending on the four distinct forms of loss functions, Bayesian and expected Bayesian estimation techniques are performed using Monte Carlo simulations to verify the effectiveness of the suggested loss function and to compare Bayesian and expected Bayesian estimation methods. Furthermore, the simulation results indicate that, depending on the minimum mean squared error, the Bayesian and expected Bayesian estimations corresponding to the weighted compound linear exponential loss function suggested in this paper have significantly better performance compared to other loss functions, and the expected Bayesian estimator also performs better than the Bayesian estimator. Finally, the proposed techniques are demonstrated using a set of real data from the medical field to clarify the applicability of the suggested estimators to real phenomena and to show that the discussed weighted compound linear exponential loss function is efficient and can be applied in a real-life scenario.
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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 (ENLF), CLLF, and WCLLF. In addition, in order to check the performance of the proposed loss function, the Bayesian estimator of WCLLF and the E-Bayesian estimator of WCLLF are used, by performing Monte Carlo simulations. The Bayesian and expected Bayesian by using the proposed loss function is compared with other methods, including maximum likelihood estimation (MLE) and Bayesian and E-Bayesian estimators under different loss functions. The simulation results show that the Bayes estimator according to WCLLF and the E-Bayesian estimator according to WCLLF proposed in this work have the best performance in estimating the shape parameters based on the least mean averaged squared error.
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Xiang, Ning, and Christopher Landschoot. "Bayesian Inference for Acoustic Direction of Arrival Analysis Using Spherical Harmonics." Entropy 21, no. 6 (June 10, 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 level of inference, Bayesian parameter estimation. This work formulates signal models using spherical harmonic beamforming that encodes the prior information on the sensor arrays in the form of analytical models with an unknown number of sound sources, and their locations. Available information on differences between the model and the sound signals as well as prior information on directions of arrivals are incorporated based on the principle of the maximum entropy. Two and three simultaneous sound sources have been experimentally tested without prior information on the number of sources. Bayesian inference provides unambiguous estimation on correct numbers of sources followed by the DoA estimations for each individual sound sources. This paper presents the Bayesian formulation, and analysis results to demonstrate the potential usefulness of the model-based Bayesian inference for complex acoustic environments with potentially multiple simultaneous sources.
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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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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, namely, the linear exponential loss, general entropy loss, and the square error loss function for estimating the two-parameter Weibull failure time distribution. These methods are compared using mean square error through simulation study with varying sample sizes. The results show that Bayesian estimator using extension of Jeffreys' prior under linear exponential loss function in most cases gives the smallest mean square error and absolute bias for both the scale parameterαand the shape parameterβfor the given values of extension of Jeffreys' prior.
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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 (June 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 prediction errors (model parameters and outputs) obtained from the trained neural network were similar to those obtained using the nonlinear Bayesian estimator.
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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 can compare the MSE and E-MSE to evaluate whose result is more effective. For the purpose of illustration in real datasets, cases of generalized Type-I hybrid censored samples are presented. In order to judge whether the sample data can be directly fitted by the Lomax distribution, we adopt the Kolmogorov–Smirnov tests for evaluation. Finally, we can get the conclusion after comparing the results of E-Bayesian and Bayesian estimation.
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Ren, Haiping, Qin Gong, and Xue Hu. "Estimation of Entropy for Generalized Rayleigh Distribution under Progressively Type-II Censored Samples." Axioms 12, no. 8 (August 10, 2023): 776. http://dx.doi.org/10.3390/axioms12080776.

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This paper investigates the problem of entropy estimation for the generalized Rayleigh distribution under progressively type-II censored samples. Based on progressively type-II censored samples, we first discuss the maximum likelihood estimation and interval estimation of Shannon entropy for the generalized Rayleigh distribution. Then, we explore the Bayesian estimation problem of entropy under three types of loss functions: K-loss function, weighted squared error loss function, and precautionary loss function. Due to the complexity of Bayesian estimation computation, we use the Lindley approximation and MCMC method for calculating Bayesian estimates. Finally, using a Monte Carlo statistical simulation, we compare the mean square errors to examine the superiority of maximum likelihood estimation and Bayesian estimation under different loss functions. An actual example is provided to verify the feasibility and practicality of various estimations.
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Gao, Huiqing, Zhanshou Chen, and Fuxiao Li. "Linear Bayesian Estimation of Misrecorded Poisson Distribution." Entropy 26, no. 1 (January 11, 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 numerical simulations and illustrative examples.
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Gustafson, Steven C., Christopher S. Costello, Eric C. Like, Scott J. Pierce, and Kiran N. Shenoy. "Bayesian Threshold Estimation." IEEE Transactions on Education 52, no. 3 (August 2009): 400–403. http://dx.doi.org/10.1109/te.2008.930092.

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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 non-parametric Parzen classi ers yield superior classi cation power. However, there is a longing for these non-parametric classi ers to posses a predictive power, such as exhibited by the odds ratio found in logistic regression (LR). The dissertation therefore dedicates a section to, amongst other things, study the paper entitled \Model-Free Objective Bayesian Prediction" (Bernardo 1999). Since this approach to Bayesian kernel density estimation is only developed for the univariate and the uncorrelated multivariate case, the section develops a theoretical multivariate approach to Bayesian kernel density estimation. This approach is theoretically capable of handling both correlated as well as uncorrelated features in data. This is done through the assumption of a multivariate Gaussian kernel function and the use of an inverse Wishart prior.
Dissertation (MSc)--University of Pretoria, 2017.
The financial assistance of the National Research Foundation (NRF) towards this research is hereby acknowledged. Opinions expressed and conclusions arrived at, are those of the authors and are not necessarily to be attributed to the NRF.
Statistics
MSc
Unrestricted
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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.
Includes bibliographical references (leaves 195-204).
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. For scenes containing a single motion we show that a wide range of previously published results are predicted by a Bayesian model that finds the most probable velocity field assuming that (1) images may be noisy and (2) velocity fields are likely to be slow and smooth. The predictions agree qualitatively, and are often in remarkable agreement quantitatively. For scenes containing multiple motions we introduce the notion of "smoothness in layers". The scene is assumed to be composed of a small number of surfaces or layers, and the motion of each layer is assumed to be slow and smooth. We again formalize these assumptions in a Bayesian framework and use the statistical technique of mixture estimation to find the predicted a surprisingly wide range of previously published results that are predicted with these simple assumptions. We discuss the shortcomings of these assumptions and show how additional assumptions can be incorporated into the same framework. Taken together, the first two parts of the thesis suggest that a seemingly complex set of illusions in human motion perception may arise from a single computational strategy that is optimal under reasonable assumptions.
(cont.) The third part of the thesis presents a computer vision algorithm that is based on the same assumptions. We compare the approach to recent developments in motion segmentation and illustrate its performance on real and synthetic image sequences.
by Yair Weiss.
Ph.D.
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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 and the best performing Taylor rule is identified using the technique called Bayesian comparison. The second study deals with development of the Small Open Economy Model with housing sector. This model is based on previous study which specifies this model as a closed economy model. I extended this model by open economy features and government sector. Czech Republic is generally considered as a small open economy and these extensions make this model more applicable to this economy. Model contains two types of households. The first type of consumers is able to access the capital markets and they can smooth consumption across time by buying or selling financial assets. These households follow the permanent income hypothesis (PIH). The other type of household uses rule of thumb (ROT) consumption, spending all their income to consumption. Other agents in this economy are specified in standard way. Outcomes of this study are mainly focused on behavior of house prices. More precisely, it means that all main outputs as Bayesian impulse response functions, Bayesian prediction and shock decomposition are focused mainly on this variable. At the end of this study one macro-prudential experiment is performed. This experiment comes up with answer on the following question: is the higher/lower Loan to Value (LTV) ratio better for the Czech Republic? This experiment is very conclusive and shows that level of LTV does not affect GDP. On the other hand, house prices are very sensitive to this LTV ratio. The recommendation for the Czech National Bank could be summarized as follows. In order to keep house prices less volatile implement rather lower LTV ratio than higher.
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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.
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 dependencies (or long memory). In this thesis I present the Bayesian estimation of the Hurst exponent. In contrast to previous methods, the Bayesian approach allows the possibility to calculate the point estimator and confidence intervals at the same time, bringing significant advantages in data-analysis as discussed in this thesis. Moreover, it is also applicable to short data and unevenly sampled data, thus broadening the range of systems where the estimation of the Hurst exponent is possible. Taking into account that one of the substantial classes of great interest in modeling is the class of Gaussian self-similar processes, this thesis considers the realizations of the processes of fractional Brownian motion and fractional Gaussian noise. Additionally, applications to real-world data, such as the data of water level of the Nile River and fixational eye movements are also discussed.
Die Abschätzung des Selbstähnlichkeitsexponenten hat in den letzten Jahr-zehnten an Aufmerksamkeit gewonnen und ist in vielen wissenschaftlichen Gebieten und Disziplinen zu einem intensiven Forschungsthema geworden. Reelle Daten, die selbsähnliches Verhalten zeigen und/oder durch den Selbstähnlichkeitsexponenten (insbesondere durch den Hurst-Exponenten) parametrisiert werden, wurden in verschiedenen Gebieten gesammelt, die von Finanzwissenschaften über Humanwissenschaften bis zu Netzwerken in der Hydrologie und dem Verkehr reichen. Diese reiche Anzahl an möglichen Anwendungen verlangt von Forschern, neue Methoden zu entwickeln, um den Selbstähnlichkeitsexponenten abzuschätzen, sowie großskalige Abhängigkeiten zu erkennen. In dieser Arbeit stelle ich die Bayessche Schätzung des Hurst-Exponenten vor. Im Unterschied zu früheren Methoden, erlaubt die Bayessche Herangehensweise die Berechnung von Punktschätzungen zusammen mit Konfidenzintervallen, was von bedeutendem Vorteil in der Datenanalyse ist, wie in der Arbeit diskutiert wird. Zudem ist diese Methode anwendbar auf kurze und unregelmäßig verteilte Datensätze, wodurch die Auswahl der möglichen Anwendung, wo der Hurst-Exponent geschätzt werden soll, stark erweitert wird. Unter Berücksichtigung der Tatsache, dass der Gauß'sche selbstähnliche Prozess von bedeutender Interesse in der Modellierung ist, werden in dieser Arbeit Realisierungen der Prozesse der fraktionalen Brown'schen Bewegung und des fraktionalen Gauß'schen Rauschens untersucht. Zusätzlich werden Anwendungen auf reelle Daten, wie Wasserstände des Nil und fixierte Augenbewegungen, diskutiert.
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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.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 135-146).
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 robust navigation and SLAM algorithms that produce accurate results when outliers and faulty measurements occur. The new algorithms address the robustness problem by augmenting the standard models used by filtering and SLAM algorithms with additional latent variables that can be used to infer when outliers have occurred. Solving the augmented problems leads to algorithms that are naturally robust to outliers and are nearly as efficient as their non-robust counterparts. The first major contribution of this thesis is a novel robust filtering algorithm that can compensate for both measurement outliers and state prediction errors using a set of sparse latent variables that can be inferred using an efficient convex optimization. Next the thesis proposes a batch robust SLAM algorithm that uses the Expectation- Maximization algorithm to infer both the navigation solution and the measurement information matrices. Inferring the information matrices allows the algorithm to reduce the impact of outliers on the SLAM solution while the Expectation-Maximization procedure produces computationally efficient calculations of the information matrix estimates. While several SLAM algorithms have been proposed that are robust to loop closure errors, to date no SLAM algorithms have been developed that are robust to landmark errors. The final contribution of this thesis is the first SLAM algorithm that is robust to both loop closure and landmark errors (incremental SLAM with consistency checking (ISCC)). ISCC adds integer variables to the SLAM optimization that indicate whether each measurement should be included in the SLAM solution. ISCC then uses an incremental greedy strategy to efficiently determine which measurements should be used to compute the SLAM solution. Evaluation on standard benchmark datasets as well as visual SLAM experiments demonstrate that ISCC is robust to a large number of loop closure and landmark outliers and that it can provide significantly more accurate solutions than state-of-the-art robust SLAM algorithms when landmark errors occur.
by Matthew C. Graham.
Ph. D.
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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.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 113-117).
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 raw sensor measurement, and provide a method to learn this function from data. This method is computationally cheap, asymptotically correct, easy to extend to new sensors, and noninvasive, in the sense that it augments, rather than disrupts, existing filtering algorithms. I additionally present two important variants; first, I extend CELLO to learn even when ground truth vehicle states are unavailable; and second, I present an equivalent Bayesian algorithm. I then use CELLO to learn covariance models for several systems, including a laser scan-matcher, an optical flow system, and a visual odometry system. I show that filtering using covariances predicted by CELLO can quantitatively improve estimator accuracy and consistency, both relative to a fixed covariance model and relative to carefully tuned domain-specific covariance models.
by William Vega-Brown.
S.M.
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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. Hoboken, NJ, USA: 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. New York, NY: 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. Berlin: Springer, 2002.

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

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

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

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

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

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

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

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Book chapters on the topic "Bayesian estimation"

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Shekhar, Shashi, and Hui Xiong. "Bayesian Estimation." In Encyclopedia of GIS, 39. Boston, MA: 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, 115–25. New York, NY: 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, 115–27. New York, NY: 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, 73–90. New York, NY: 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, 397–431. Cham: 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, 77–94. Cham: 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, 237–58. Boca Raton, FL: CRC Press, 2015- | Series: Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences.: 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, 1–25. Berlin, Heidelberg: 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, 137–77. New York, NY: Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4757-4314-2_4.

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Mills, Jeffrey A., and Olivier Parent. "Bayesian MCMC Estimation." In Handbook of Regional Science, 1571–95. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-23430-9_89.

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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 viewpoint. As the result, it was revealed that the minimum ABIC did not always provide the best estimates from the viewpoint of wave estimation and the simply modified response functions could reduce the estimating errors in some cases. The reasons were considered that the estimating error at the sharp peak of response amplitude operators was closely related to existence of the local minima of the ABIC surface and the simply modified response functions had some effects to make the ABIC surface smoother. It is pointed out as the conclusion of this report that any estimating errors of the ship response functions were not considered in the Bayesian modeling.
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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. Ft. Collins, CO: 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. Ft. Collins, CO: 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. Fort Belvoir, VA: Defense Technical Information Center, October 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. Fort Belvoir, VA: Defense Technical Information Center, September 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. Fort Belvoir, VA: Defense Technical Information Center, September 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. Fort Belvoir, VA: Defense Technical Information Center, August 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. Bogotá, Colombia: Banco de la República, October 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. Fort Belvoir, VA: Defense Technical Information Center, February 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. Cambridge, MA: National Bureau of Economic Research, March 2021. http://dx.doi.org/10.3386/w28617.

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Álvarez Florens Odendahl, Luis J., and Germán López-Espinosa. Data outliers and Bayesian VARs in the euro area. Madrid: Banco de España, November 2022. http://dx.doi.org/10.53479/23552.

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We propose a method to adjust for data outliers in Bayesian Vector Autoregressions (BVARs), which allows for different outlier magnitudes across variables and rescales the reduced form error terms. We use the method to document several facts about the effect of outliers on estimation and out-of-sample forecasting results using euro area macroeconomic data. First, the COVID-19 pandemic led to large swings in macroeconomic data that distort the BVAR estimation results. Second, these swings can be addressed by rescaling the shocks’ variance. Third, taking into account outliers before 2020 leads to mild improvements in the point forecasts of BVARs for some variables and horizons. However, the density forecast performance considerably deteriorates. Therefore, we recommend taking into account outliers only on pre-specified dates around the onset of the COVID-19 pandemic.
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