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Books on the topic 'Hierarchical Bayesian Modeling'

Author: Grafiati

Published: 4 June 2021

Last updated: 7 July 2024

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1

Lawson, Andrew. Bayesian disease mapping: Hierarchical modeling in spatial epidemiology. Boca Raton: Taylor & Francis, 2008.

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2

1957-, Clark James Samuel, and Gelfand Alan E. 1945-, eds. Hierarchical modelling for the environmental sciences: Statistical methods and applications. Oxford: Oxford University Press, 2006.

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3

Parent, Eric, and Etienne Rivot. Introduction to Hierarchical Bayesian Modeling for Ecological Data. Chapman and Hall/CRC, 2012. http://dx.doi.org/10.1201/b12501.

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4

Bayesian Disease Mapping Hierarchical Modeling In Spatial Epidemiology. Taylor & Francis Inc, 2013.

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5

Lawson, Andrew B. Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology. Taylor & Francis Group, 2008.

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6

Rivot, Etienne, and Éric Parent. Introduction to Hierarchical Bayesian Modeling for Ecological Data. Taylor & Francis Group, 2012.

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7

Lawson, Andrew B. Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology. Taylor & Francis Group, 2013.

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8

Parent, Éric. Introduction to Hierarchical Bayesian Modeling for Ecological Data. Taylor & Francis Group, 2012.

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9

Parent, Eric, and Etienne Rivot. Introduction to Hierarchical Bayesian Modeling for Ecological Data. Taylor & Francis Group, 2012.

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10

Keiding, Niels, Andrew B. Lawson, Terry Speed, Byron J. Morgan, and Peter Van Der Heijden. Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology. Taylor & Francis Group, 2008.

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11

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 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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12

Chin, Hoong Chor, and Helai Huang. Modeling Multilevel Data in Traffic Safety: A Bayesian Hierarchical Approach. Nova Science Pub Inc, 2013.

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13

Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology, Third Edition. Chapman and Hall/CRC, 2018.

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14

Lawson, Andrew B. Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology, Third Edition. Taylor & Francis Group, 2018.

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15

Lawson, Andrew B. Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology, Third Edition. Taylor & Francis Group, 2018.

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16

Lawson, Andrew B. Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology, Second Edition. Taylor & Francis Group, 2013.

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17

Lawson, Andrew B. Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology, Third Edition. Taylor & Francis Group, 2018.

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18

Lawson, Andrew B. Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology, Third Edition. Taylor & Francis Group, 2018.

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19

(Editor), James S. Clark, and Alan Gelfand (Editor), eds. Hierarchical Modelling for the Environmental Sciences. Oxford University Press, USA, 2006.

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20

Liang, Percy, Michael Jordan, and Dan Klein. Probabilistic grammars and hierarchical Dirichlet processes. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.27.

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This article focuses on the use of probabilistic context-free grammars (PCFGs) in natural language processing involving a large-scale natural language parsing task. It describes detailed, highly-structured Bayesian modelling in which model dimension and complexity responds naturally to observed data. The framework, termed hierarchical Dirichlet process probabilistic context-free grammar (HDP-PCFG), involves structured hierarchical Dirichlet process modelling and customized model fitting via variational methods to address the problem of syntactic parsing and the underlying problems of grammar induction and grammar refinement. The central object of study is the parse tree, which can be used to describe a substantial amount of the syntactic structure and relational semantics of natural language sentences. The article first provides an overview of the formal probabilistic specification of the HDP-PCFG, algorithms for posterior inference under the HDP-PCFG, and experiments on grammar learning run on the Wall Street Journal portion of the Penn Treebank.

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21

Carvalho, Carlos, and Jill Rickershauser. Characterizing the uncertainty of climate change projections using hierarchical models. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.20.

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This article focuses on the use of Bayesian hierarchical models for integration and comparison of predictions from multiple models and groups, and more specifically for characterizing the uncertainty of climate change projections. It begins with a discussion of the current state and future scenarios concerning climate change and human influences, as well as various models used in climate simulations and the goals and challenges of analysing ensembles of opportunity. It then introduces a suite of statistical models that incorporate output from an ensemble of climate models, referred to as general circulation models (GCMs), with the aim of reconciling different future projections of climate change while characterizing their uncertainty in a rigorous fashion. Posterior distributions of future temperature and/or precipitation changes at regional scales are obtained, accounting for many peculiar data characteristics. The article confirms the reasonableness of the Bayesian modelling assumptions for climate change projections' uncertainty analysis.

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22

Green, Peter, Kanti Mardia, Vysaul Nyirongo, and Yann Ruffieux. Bayesian modelling for matching and alignment of biomolecules. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.2.

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This article describes Bayesian modelling for matching and alignment of biomolecules. One particular task where statistical modelling and inference can be useful in scientific understanding of protein structure is that of matching and alignment of two or more proteins. In this regard, statistical shape analysis potentially has something to offer in solving biomolecule matching and alignment problems. The article discusses the use of Bayesian methods for shape analysis to assist with understanding the three-dimensional structure of protein molecules, with a focus on the problem of matching instances of the same structure in the CoMFA (Comparative Molecular Field Analysis) database of steroid molecules. It introduces a Bayesian hierarchical model for pairwise matching and for alignment of multiple configurations before concluding with an overview of some advantages of the Bayesian approach to problems in protein bioinformatics, along with modelling and computation issues, alternative approaches, and directions for future research.

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23

(Editor), James S. Clark, and Alan Gelfand (Editor), eds. Hierarchical Modelling for the Environmental Sciences: Statistical Methods and Applications (Oxford Biology). Oxford University Press, USA, 2006.

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24

Hierarchical Modelling of Discrete Longitudinal Data: Applications of Markov Chain Monte Carlo. Munich, Germany: Herbert Witz Verlag, Wissenschaft, 1997.

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25

O'Hagan, Anthony, and Mike West, eds. The Oxford Handbook of Applied Bayesian Analysis. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.001.0001.

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This handbook discusses various applications of modern Bayesian analysis in important and challenging problems. With contributions from leading researchers and practitioners in interdisciplinary Bayesian analysis, the book highlights current frontiers of research in each application. Each chapter involves a concise review of the application area, describes the problem contexts and goals, discusses aspects of the data and overall statistical issues, and offers detailed analysis with relevant Bayesian models and methods. The book is organised into five sections based on the field of application, namely: Biomedical and Health Sciences; Industry, Economics and Finance; Environment and Ecology; Policy, Political and Social Sciences; and Natural and Engineering Sciences. Topics range from an epidemiological study involving pregnancy outcomes, to matching and alignment of biomolecules; pharmaceutical testing from multiple clinical trials concerned with side-effects and adverse events; malaria mapping in the Amazon rain forest; risk assessment of contamination of farm-pasteurized milk with the bacterium Vero-cytotoxigenic E. coli (VTEC) O157; Bayesian analysis and decision making in the maintenance and reliability of nuclear power plants; risk modelling regarding speculative trading strategies in financial futures markets; the use of hierarchical models to characterize the uncertainty of climate change projections; and the use of multistate models for mental fatigue.

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26

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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