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Books on the topic 'Bayesian hierarchical spatiotemporal models'

Author: Grafiati

Published: 13 July 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

Thériault, Marc-Erick. Bayesian hierarchical models for mapping lung cancer mortality in Ontario. Ottawa: National Library of Canada, 2000.

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4

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

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5

Vanem, Erik. Bayesian Hierarchical Space-Time Models with Application to Significant Wave Height. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-30253-4.

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6

Congdon, Peter D. Bayesian Hierarchical Models. Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9780429113352.

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7

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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8

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

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9

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

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10

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

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11

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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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

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

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14

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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15

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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16

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

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17

(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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18

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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19

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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20

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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21

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 to understand how information integration must work computationally – at least in approximation – also in the brain. Bayesian networks in the form of graphical models allow the modularization of information and the factorization of interactions, which can strongly improve the efficiency of generative models. The resulting generative models essentially produce state estimations in the form of probability densities, which are very well-suited to integrate multiple sources of information, including top-down and bottom-up ones. A hierarchical neural visual processing architecture illustrates this point even further. Finally, some well-known visual illusions are shown and the perceptions are explained by means of generative, information integrating, perceptual processes, which in all cases combine top-down prior knowledge and expectations about objects and environments with the available, bottom-up visual information.

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