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Thematische Bibliographien / Bayesian estimate / Bücher

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Bücher zum Thema „Bayesian estimate“

Autor: Grafiati

Veröffentlicht am 28. Juni 2021

Zuletzt aktualisiert am 21. Juni 2025

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1

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

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2

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

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3

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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4

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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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

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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7

ha-meḥḳar, Banḳ Yiśraʼel Maḥleḳet, ed. The choice of a foreign price measure in a Bayesian estimated New-Keynesian model for Israel. Research Department, Bank of Israel, 2009.

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8

Walsh, Bruce, and Michael Lynch. Analysis of Short-term Selection Experiments: 2. Mixed-model and Bayesian Approaches. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198830870.003.0019.

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When the full pedigree of individuals whose values (records) were used in the selection decisions during an experiment (or breeding program) is known, LS analysis can be replaced by mixed models and their Bayesian extensions. In this setting, REML can be used to estimate genetic variances and BLUP can be used to estimate the mean breeding value in any given generation. The latter allows for genetic trends to be separated from environmental trends without the need for a control population. Under the infinitesimal model setting (wherein selection-induced allele-frequency changes are small during the course of the experiment), the use of the relationship matrix in a BLUP analysis accounts for drift, nonrandom mating, and linkage disequilibrium.

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9

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 for the year 2007 for three market indices: the Standard and Poor’s S&P500, the NASDAQ NDX100 and the financial equity index called XLF. Three models of financial time series are estimated: a model with stochastic volatility, a model with stochastic volatility that also incorporates jumps in volatility, and a Garch model.

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10

Higdon, Dave, Katrin Heitmann, Charles Nakhleh, and Salman Habib. Combining simulations and physical observations to estimate cosmological parameters. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.26.

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This article focuses on the use of a Bayesian approach that combines simulations and physical observations to estimate cosmological parameters. It begins with an overview of the Λ-cold dark matter (CDM) model, the simplest cosmological model in agreement with the cosmic microwave background (CMB) and largescale structure analysis. The CDM model is determined by a small number of parameters which control the composition, expansion and fluctuations of the universe. The present study aims to learn about the values of these parameters using measurements from the Sloan Digital Sky Survey (SDSS). Computationally intensive simulation results are combined with measurements from the SDSS to infer about a subset of the parameters that control the CDM model. The article also describes a statistical framework used to determine a posterior distribution for these cosmological parameters and concludes by showing how it can be extended to include data from diverse data sources.

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11

Chappell, Michael, Bradley MacIntosh, and Thomas Okell. Kinetic Modeling. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198793816.003.0004.

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The quantification of perfusion from arterial spin labeling (ASL) perfusion MRI data relies upon the principles of tracer kinetics. This chapter first outlines the simplest form of a tracer kinetic model that can be applied to ASL data, before exploring variations on this model that can be applied to extract other hemodynamic information such as arterial transit time. Finally, the chapter examines how tracer kinetic models are used with data to estimate perfusion parameters, including the use of model fitting and Bayesian inference.

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12

Gelfand, Alan, and Sujit K. Sahu. Models for demography of plant populations. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.17.

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This article discusses the use of Bayesian analysis and methods to analyse the demography of plant populations, and more specifically to estimate the demographic rates of trees and how they respond to environmental variation. It examines data from individual (tree) measurements over an eighteen-year period, including diameter, crown area, maturation status, and survival, and from seed traps, which provide indirect information on fecundity. The multiple data sets are synthesized with a process model where each individual is represented by a multivariate state-space submodel for both continuous (fecundity potential, growth rate, mortality risk, maturation probability) and discrete states (maturation status). The results from plant population demography analysis demonstrate the utility of hierarchical modelling as a mechanism for the synthesis of complex information and interactions.

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13

Size matters: measuring the effects of inequality and growth shocks. UNU-WIDER, 2020. http://dx.doi.org/10.35188/unu-wider/2020/934-1.

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Understanding the relationship between income inequality and economic growth is of utmost importance to economists and social scientists. In this paper we use a Bayesian structural vector autoregression approach to estimate the relationship between inequality and growth via growth and inequality shocks for two large economies, China and the USA, for the years 1979–2018. We find that a growth shock is inequality-increasing, and an inequality shock is growth-reducing. We also find, however, that the sizes of the effects of these shocks are very small, accounting for under 2 per cent of the variance for both countries. Finally, we also find that the effects of the shocks dissipate within ten years, suggesting that the effects of these shocks are a short-term phenomenon.

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14

Harjes, Thomas, and Luca Antonio Ricci. Bayesian-Estimated Model of Inflationtargeting in South Africa. International Monetary Fund, 2008.

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15

Harjes, Thomas, and Luca Antonio Ricci. Bayesian-Estimated Model of Inflationtargeting in South Africa. International Monetary Fund, 2008.

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16

Harjes, Thomas, and Luca Antonio Ricci. Bayesian-Estimated Model of Inflationtargeting in South Africa. International Monetary Fund, 2008.

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17

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

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18

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

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19

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 sampling. MAPIS is compared with ML and the MCMC methods. The MCMC methods produce multimodal posterior parameter distributions in overfitted models. This results in the posterior distribution of k being biased towards high k. It is shown that MAPIS does not suffer from this problem. A simple numerical example is discussed.

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20

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 stochastic optimization model which finds the optimal replacement time for a system that randomly fails over a finite horizon. The article first introduces the model for maintenance and reliability analysis before presenting the optimization results. It also examines the nuclear power plant data to be used in the Bayesian models.

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21

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 major islands across the region and provides recommendations for future research and chronology building, highlighting the potential for Bayesian analyses. Estimates for the colonization of Hawai'i are presented as a case study.

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22

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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23

Schmidt, Alexandra, Jennifer Hoeting, João Batista M. Pereira, and Pedro Paulo Vieira. Mapping malaria in the Amazon rain forest: A spatio-temporal mixture model. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.5.

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This article focuses on the use of a spatio-temporal mixture model for mapping malaria in the Amazon rain forest. The spatio-temporal model was developed to study malaria outbreaks over a four year period in the state of Amazonas, Brazil. The goal is to predict malaria counts for unobserved municipalities and future time periods with the aid of a free-form spatial covariance structure and a methodology that allows temporal prediction and spatial interpolation for outbreaks of malaria over time. The proposed structure is unique in that it is not a distance- or neighbourhood-based covariance model. Instead, spatial correlation is allowed among all locations to be estimated freely. To model the temporal correlation between observations, a Bayesian dynamic linear model is incorporated into one level of the spatio-temporal mixture model. The model also provides sensible ways of malaria mapping for municipalities which were not observed.

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24

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 information processing. Understanding the nature and origins of that strange inversion, Dennett believes, is thus key to understanding the nature and origins of human experience itself. This paper examines this claim, paying special attention to recent formulations that link that strange inversion to the emerging vision of the brain as a Bayesian estimator, constantly seeking to predict the unfolding sensory barrage.

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