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Books on the topic 'Bayesian parameter estimation'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

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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2

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

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3

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

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4

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

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5

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

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6

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

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7

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

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8

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

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9

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

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10

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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11

Chen, Han-Fu, and Wenxiao Zhao. Recursive Identification and Parameter Estimation. Taylor & Francis Group, 2014.

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12

Van Trees, Harry L., and Kristine L. Bell. Bayesian Bounds for Parameter Estimation and Nonlinear Filtering/Tracking. IEEE, 2007. http://dx.doi.org/10.1109/9780470544198.

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13

Harry L. Van Trees (Editor) and Kristine L. Bell (Editor), eds. Bayesian Bounds for Parameter Estimation and Nonlinear Filtering/Tracking. Wiley-IEEE Press, 2007.

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14

Chen, Hanfu, and Wenxiao Zhao. Recursive Identification and Parameter Estimation. Taylor & Francis Group, 2017.

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15

Chen, Han-Fu, and Wenxiao Zhao. Recursive Identification and Parameter Estimation. Taylor & Francis Group, 2014.

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16

Recursive Identification and Parameter Estimation. Taylor & Francis Group, 2014.

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17

Parameter Estimation of Sinusodial Signals in Noise: Theory and Methods. Chapman & Hall/CRC, 2002.

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18

Donovan, Therese, and Ruth M. Mickey. Bayesian Statistics for Beginners. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198841296.001.0001.

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Bayesian Statistics for Beginners is an entry-level book on Bayesian statistics. It is like no other math book you’ve read. It is written for readers who do not have advanced degrees in mathematics and who may struggle with mathematical notation, yet need to understand the basics of Bayesian inference for scientific investigations. Intended as a “quick read,” the entire book is written as an informal, humorous conversation between the reader and writer—a natural way to present material for those new to Bayesian inference. The most impressive feature of the book is the sheer length of the journ

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19

Quintana, José Mario, Carlos Carvalho, James Scott, and Thomas Costigliola. Extracting S&P500 and NASDAQ Volatility: The Credit Crisis of 2007–2008. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.13.

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This article demonstrates the utility of Bayesian modelling and inference in financial market volatility analysis, using the 2007-2008 credit crisis as a case study. It first describes the applied problem and goal of the Bayesian analysis before introducing the sequential estimation models. It then discusses the simulation-based methodology for inference, including Markov chain Monte Carlo (MCMC) and particle filtering methods for filtering and parameter learning. In the study, Bayesian sequential model choice techniques are used to estimate volatility and volatility dynamics for daily data fo

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20

Go, Delfin S., Hans Lofgren, Fabian Mendez Ramos, and Sherman Robinson. Estimating Parameters and Structural Change in CGE Models Using a Bayesian Cross-Entropy Estimation Approach. The World Bank, 2015. http://dx.doi.org/10.1596/1813-9450-7174.

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21

Martin, Andrew D. Bayesian Analysis. Edited by Janet M. Box-Steffensmeier, Henry E. Brady, and David Collier. Oxford University Press, 2009. http://dx.doi.org/10.1093/oxfordhb/9780199286546.003.0021.

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This article surveys modern Bayesian methods of estimating statistical models. It first provides an introduction to the Bayesian approach for statistical inference, contrasting it with more conventional approaches. It then explains the Monte Carlo principle and reviews commonly used Markov Chain Monte Carlo (MCMC) methods. This is followed by a practical justification for the use of Bayesian methods in the social sciences, and a number of examples from the literature where Bayesian methods have proven useful are shown. The article finally provides a review of modern software for Bayesian infer

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22

Heiner, Karl W., Marc Kennedy, and Anthony O'Hagan. Sequential multilocation auditing and the New York food stamps program. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.23.

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This article discusses the use of Bayesian methods in analysing data that evolve over time in sequential multilocation auditing. Using the New York food stamps program as a case study, it proposes a model that incorporates a nonparametric component for the error magnitudes (taints), a hierarchical model for overall error rates across counties and parameters controlling the variation of rates from one year to the next, including an overall trend in error rates. The article first provides an overview of the New York food stamps program, along with the auditing concepts and terminology, before in

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