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Bayesian Inference on Complicated Data

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  • Niansheng Tang

Abstract

Due to great applications in various fields, such as social science, biomedicine, genomics, and signal processing, and the improvement of computing ability, Bayesian inference has made substantial developments for analyzing complicated data. This book introduces key ideas of Bayesian sampling methods, Bayesian estimation, and selection of the prior. It is structured around topics on the impact of the choice of the prior on Bayesian statistics, some advances on Bayesian sampling methods, and Bayesian inference for complicated data including breast cancer data, cloud-based healthcare data, gene network data, and longitudinal data. This volume is designed for statisticians, engineers, doctors, and machine learning researchers.

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Suggested Citation

  • Niansheng Tang (ed.), 2020. "Bayesian Inference on Complicated Data," Books, IntechOpen, number 6440, January-J.
    • Handle: RePEc:ito:pbooks:6440
    DOI: 10.5772/intechopen.83214
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    JEL classification:

    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General

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