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1

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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2

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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3

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

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4

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

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5

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

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6

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

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7

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

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8

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

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9

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

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10

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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11

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

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12

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

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13

Smith, C. Ray, and Gary J. Erickson, eds. Maximum-Entropy and Bayesian Spectral Analysis and Estimation Problems. Dordrecht: Springer Netherlands, 1987. http://dx.doi.org/10.1007/978-94-009-3961-5.

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14

Shemyakin, Arkady, and Alexander Kniazev. Introduction to Bayesian Estimation and Copula Models of Dependence. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2017. http://dx.doi.org/10.1002/9781118959046.

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15

Ardia, David. Financial Risk Management with Bayesian Estimation of GARCH Models. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-78657-3.

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16

Pilz, Jürgen. Bayesian estimation and experimental design in linear regression models. 2nd ed. Chichester: Wiley, 1991.

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17

Park, In Kyoung. Software cost estimation through Bayesian inference of software size. Monterey, Calif: Naval Postgraduate School, 1985.

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18

Pilz, Jürgen. Bayesian estimation and experimental design in linear regression models. Chichester: Wiley, 1991.

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19

Recursive nonlinear estimation: A geometric approach. Berlin: Springer, 1996.

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20

Bhat, Avanindra Narayan. Bayesian Inference in Econometrics. Delhi, India: B.R. Publishing Corporation, 1999.

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21

Lynch, Scott M., ed. Introduction to Applied Bayesian Statistics and Estimation for Social Scientists. New York, NY: Springer New York, 2007. http://dx.doi.org/10.1007/978-0-387-71265-9.

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22

Samaniego, Francisco J. A Comparison of the Bayesian and Frequentist Approaches to Estimation. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-5941-6.

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23

Samaniego, Francisco J. A comparison of the Bayesian and frequentist approaches to estimation. New York: Springer, 2010.

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24

Bolstad, William M. Robust Bayesian estimation using variable mixture of conjugate family members. Hamilton, N.Z: University of Waikato, 1990.

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25

Sun, Li. Bayesian estimation procedures for one and two-way hierarchical models. Toronto: [s.n.], 1992.

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26

Large-scale inference: Empirical Bayes methods for estimation, testing, and prediction. Cambridge: Cambridge University Press, 2010.

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27

Grycko, Eugen. Zur Bayesschen Theorie mengenwertiger Entscheidungsfunktionen. Frankfurt am Main: A. Hain, 1993.

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28

Press, S. James. Empirical Bayes estimation of the mean in a multivariate normal distribution. Santa Monica, CA: Rand, 1986.

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29

Lim, Chee Peng. Probabilistic fuzzy ARTMAP: An autonomous neural network architecture for Bayesian probability estimation. Sheffield: University of Sheffield, Dept. of Automatic Control & Systems Engineering, 1995.

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30

Financial risk management with Bayesian estimation of GARCH models: Theory and applications. Berlin: Springer, 2008.

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31

Szrede, Miroslaw. Subjective probability distributions in Bayesian estimation of all-excess-demand models (revised paper). Leicester: University of Leicester, Department of Economics, 1992.

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32

Dueker, Michael. Kalman filtering with truncated normal state variables for Bayesian estimation of macroeconomic models. [St. Louis, Mo.]: Federal Reserve Bank of St. Louis, 2005.

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33

Bera, Anil K. Estimation of systematic risk using Bayesian analysis with hierarchical and non-normal priors. [Urbana, Ill.]: College of Commerce and Business Administration, University of Illinois Urbana-Champaign, 1989.

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34

Françoise, Prêteux, Mohammad-Djafari Ali, Dougherty Edward R, Society of Photo-optical Instrumentation Engineers., and Society for Industrial and Applied Mathematics., eds. Mathematical modeling, Bayesian estimation, and inverse problems: 21-23 July 1999, Denver, Colorado. Bellingham, Wash., USA: SPIE, 1999.

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35

Męczarski, Marek. Problemy odporności w bayesowskiej analizie statystycznej. Warszawa: Szkoła Główna Handlowa, 1998.

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36

Haug, Anton J. Bayesian Estimation and Tracking. Wiley & Sons, Incorporated, John, 2012.

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37

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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Abstract:
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 very flexible and straightforward for estimating parameters of complex hierarchical models (and simpler models too). We provide an introduction to the ideas of hierarchical models and to the Bayesian estimation of their parameters, illustrated with two extended examples. One example considers baseball batting averages of individual players grouped by fielding position. A second example uses a hierarchical extension of a cognitive process model to examine individual differences in attention allocation of people who have eating disorders. We conclude by discussing Bayesian model comparison as a case of hierarchical modeling.
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38

Bryant, John, and Junni L. Zhang. Bayesian Demographic Estimation and Forecasting. Chapman and Hall/CRC, 2018. http://dx.doi.org/10.1201/9780429452987.

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39

Bayesian Demographic Estimation and Forecasting. Taylor & Francis Group, 2020.

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40

John, Bryant, and Junni L. Zhang. Bayesian Demographic Estimation and Forecasting. Taylor & Francis Group, 2018.

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41

Bayesian Estimation of DSGE Models. Princeton University Press, 2016.

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42

Bayesian Demographic Estimation and Forecasting. Taylor & Francis Group, 2018.

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43

John, Bryant, and Junni L. Zhang. Bayesian Demographic Estimation and Forecasting. Taylor & Francis Group, 2018.

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44

John, Bryant, and Junni L. Zhang. Bayesian Demographic Estimation and Forecasting. Taylor & Francis Group, 2018.

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45

John, Bryant, and Junni L. Zhang. Bayesian Demographic Estimation and Forecasting. Taylor & Francis Group, 2018.

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46

Schorfheide, Frank, and Edward P. Herbst. Bayesian Estimation of DSGE Models. Princeton University Press, 2015.

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47

Maliszewski, Wojciech. Vietnam: Bayesian Estimation of Output Gap. International Monetary Fund, 2010.

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48

Feldman, Jacob, Manish Singh, and Vicky Froyen. Perceptual Grouping as Bayesian Mixture Estimation. Edited by Sergei Gepshtein, Larry Maloney, and Manish Singh. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780199829347.013.5.

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49

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

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50

Maliszewski, Wojciech. Vietnam: Bayesian Estimation of Output Gap. International Monetary Fund, 2010.

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