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Books on the topic 'Bayesian estimators'

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

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1

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

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2

Gruber, Marvin H. J. Regression estimators: A comparative study. Academic Press, 1990.

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3

Gruber, Marvin H. J. Regression estimators: A comparative study. 2nd ed. Johns Hopkins University Press, 2010.

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4

Reiser, Benjamin. A comparison of three point estimators for P(Y. University of Toronto, Dept. of Statistics, 1985.

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5

Tanabe, Kunio. BNDE, FORTRAN subroutines for computing Bayesian nonparametric univariate and bivariate density estimator. Institute of Statistical Mathematics, 1988.

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6

Houston, Walter M. Empirical Bayes estimates of parameters from the logistic regression model. ACT, Inc., 1997.

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7

Houston, Walter M. Empirical Bayes estimates of parameters from the logistic regression model. ACT, Inc., 1997.

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8

1972-, Raymer James, Willekens Frans, and University of Southampton. Division of Social Statistics., eds. International migration in Europe: Data, models and estimates. Wiley, 2008.

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9

Doppelhofer, Gernot. Determinants of long-term growth: A Bayesian averaging of classical estimates (BACE) approach. National Bureau of Economic Research, 2000.

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10

Torres, Maura Acevedo. Reduction of Uncertainty in Post-Event Seismic Loss Estimates Using Observation Data and Bayesian Updating. [publisher not identified], 2017.

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11

Regression estimators: A comparative study. 2nd ed. Johns Hopkins University Press, 2010.

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12

Raymer, James, and Frans Wiilekens. International Migration in Europe: Data, Models and Estimates. Wiley & Sons, Limited, John, 2008.

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13

Raymer, James, and Frans Wiilekens. International Migration in Europe: Data, Models and Estimates. Wiley & Sons, Incorporated, John, 2008.

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14

Cheng, Russell. Finite Mixture Models. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198505044.003.0017.

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Fitting a finite mixture model when the number of components, k, is unknown can be carried out using the maximum likelihood (ML) method though it is non-standard. Two well-known Bayesian Markov chain Monte Carlo (MCMC) methods are reviewed and compared with ML: the reversible jump method and one using an approximating Dirichlet process. Another Bayesian method, to be called MAPIS, is examined that first obtains point estimates for the component parameters by the maximum a posteriori method for different k and then estimates posterior distributions, including that for k, using importance sampli
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15

Cumming, Jonathan A., and Michael Goldstein. Bayesian analysis and decisions in nuclear power plant maintenance. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.9.

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This article discusses the results of a study in Bayesian analysis and decision making in the maintenance and reliability of nuclear power plants. It demonstrates the use of Bayesian parametric and semiparametric methodology to analyse the failure times of components that belong to an auxiliary feedwater system in a nuclear power plant at the South Texas Project (STP) Electric Generation Station. The parametric models produce estimates of the hazard functions that are compared to the output from a mixture of Polya trees model. The statistical output is used as the most critical input in a stoc
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16

Rieth, Timothy, and Ethan E. Cochrane. The Chronology of Colonization in Remote Oceania. Edited by Ethan E. Cochrane and Terry L. Hunt. Oxford University Press, 2015. http://dx.doi.org/10.1093/oxfordhb/9780199925070.013.010.

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Colonization of Remote Oceania resulted in the discovery of thousands of islands spread across an enormous area of the Pacific Ocean. Beginning as early as approximately 3500 cal. B.P. in Western Micronesia, populations began an expansion westward eventually settling East Polynesia over two millennia later. Although this general pattern is well-established, the reliability of colonization chronologies for particular islands and island groups varies significantly. This chapter synthesizes and critiques current interpretations of radiocarbon and other dating estimates for colonization of the maj
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17

Butz, Martin V., and Esther F. Kutter. Top-Down Predictions Determine Perceptions. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780198739692.003.0009.

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While bottom-up visual processing is important, the brain integrates this information with top-down, generative expectations from very early on in the visual processing hierarchy. Indeed, our brain should not be viewed as a classification system, but rather as a generative system, which perceives something by integrating sensory evidence with the available, learned, predictive knowledge about that thing. The involved generative models continuously produce expectations over time, across space, and from abstracted encodings to more concrete encodings. Bayesian information processing is the key t
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18

Abdel-Fattah, Abdel-Fattah A. Accuracy of item response theory parameter estimates using maximum likelihood and Bayesian procedures as implemented in LOGIST and BILOG. 1990.

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19

Clark, Andy. Strange Inversions. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199367511.003.0013.

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Strange inversions occur when things work in ways that turn received wisdom upside down. Hume offered a strangely inverted story about causation, and Darwin, about apparent design. Dennett suggests that a strange inversion also occurs when we project our own reactive complexes outward, painting our world with elusive properties like cuteness, sweetness, blueness, sexiness, funniness, and more. Such properties strike us as experiential causes, but they are (Dennett argues) really effects—a kind of shorthand for whole sets of reactive dispositions rooted in the nuts and bolts of human informatio
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