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        Bayesian Methods for Statistical Analysis

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        Author(s)
        Puza, Borek
        Language
        English
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        Abstract
        Bayesian methods for statistical analysis is a book on statistical methods for analysing a wide variety of data. The book consists of 12 chapters, starting with basic concepts and covering numerous topics, including Bayesian estimation, decision theory, prediction, hypothesis testing, hierarchical models, Markov chain Monte Carlo methods, finite population inference, biased sampling and nonignorable nonresponse. The book contains many exercises, all with worked solutions, including complete computer code. It is suitable for self-study or a semester-long course, with three hours of lectures and one tutorial per week for 13 weeks.
        URI
        http://library.oapen.org/handle/20.500.12657/32424
        Keywords
        statistics; mathematics; bayesian inference; probability; Algorithm; Confidence interval; Histogram; Monte Carlo method; Posterior probability; Sampling (statistics); WinBUGS
        DOI
        10.26530/OAPEN_611011
        ISBN
        9781921934254
        OCN
        1076716258
        Publisher
        ANU Press
        Publisher website
        https://press.anu.edu.au/
        Publication date and place
        2015
        Classification
        Mathematics
        Probability and statistics
        Bayesian inference
        Public remark
        Relevant Wikipedia pages: Algorithm - https://en.wikipedia.org/wiki/Algorithm; Bayesian inference - https://en.wikipedia.org/wiki/Bayesian_inference; Confidence interval - https://en.wikipedia.org/wiki/Confidence_interval; Histogram - https://en.wikipedia.org/wiki/Histogram; Monte Carlo method - https://en.wikipedia.org/wiki/Monte_Carlo_method; Posterior probability - https://en.wikipedia.org/wiki/Posterior_probability; Probability - https://en.wikipedia.org/wiki/Probability; Sampling (statistics) - https://en.wikipedia.org/wiki/Sampling_(statistics); WinBUGS - https://en.wikipedia.org/wiki/WinBUGS
        Rights
        http://press.anu.edu.au/about/conditions-use
        • Imported or submitted locally

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        License

        • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

        Credits

        • logo EU
        • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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