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Books on the topic 'Hierarchal Bayesian statistics'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Congdon, P. Applied Bayesian hierarchical methods. Boca Raton: Chapman & Hall/CRC, 2010.

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2

Congdon, P. Applied Bayesian hierarchical methods. Boca Raton: Chapman & Hall/CRC, 2010.

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3

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

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4

Kéry, Marc. Bayesian population analysis using WinBUGS: A hierarchical perspective. Boston: Academic Press, 2011.

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5

Bayesian Random Effect and Other Hierarchical Models: An Applied Perspective. Chapman & Hall/CRC, 2009.

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6

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

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7

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

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8

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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9

Computational statistics: Hierarchical Bayes and MCMC methods in the environmental sciences. New York: Oxford University Press, 2006.

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10

(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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11

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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12

Cemgil, A. Taylan, Simon Godsill, Paul Peeling, and Nick Whiteley. Bayesian statistical methods for audio and music processing. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.25.

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This article focuses on the use of Bayesian statistical methods in audio and music processing in the context of an application to multipitch audio and determining a musical ‘score’ representation that includes pitch and time duration summary for a musical extract (the so-called ‘piano-roll’ representation of music). It first provides an overview of mainstream applications of audio signal processing, the properties of musical audio, superposition and how to address it using the Bayesian approach, and the principal challenges facing audio processing. It then considers the fundamental audio processing tasks before discussing a range of Bayesian hierarchical models involving both time and frequency domain dynamic models. It shows that Bayesian analysis is applicable in audio signal processing in real environments where acoustical conditions and sound sources are highly variable, yet audio signals possess strong statistical structure.

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13

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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14

Bayesian Hierarchical Space-Time Models with Application to Significant Wave Height. Springer, 2013.

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15

Bitner-Gregersen, Elzbieta Maria, Christopher K. Wikle, and Erik Vanem. Bayesian Hierarchical Space-Time Models with Application to Significant Wave Height. Springer, 2016.

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16

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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17

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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