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A Calibration-Based Method in Computing Bayesian Posterior Distributions with Applications in Stock Market

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Predictive Econometrics and Big Data (TES 2018)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 753))

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Abstract

Finding effective methods to compute or estimate posterior distributions of model parameters is of paramount importance in Bayesian statistics. In fact, Bayesian inference has only been extraordinarily popular in applications after the births of efficient algorithms like the Monte Carlo Markov Chain. Practicality of posterior distributions depends heavily on the combination of likelihood functions and prior distributions. In certain cases, closed-form formulas for posterior distributions can be attained; in this paper, based on the theory of distortion functions, a calibration-like method to calculate explicitly the posterior distributions for three crucial models, namely the normal, Poisson and Bernoulli is introduced. The paper ends with some applications in stock market.

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Author information

Authors and Affiliations

  1. University of Economics and Law, VNU-HCM, Ho Chi Minh City, Vietnam

    Dung Tien Nguyen, Son P. Nguyen, Uyen H. Pham & Thien Dinh Nguyen

Authors
  1. Dung Tien Nguyen
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  2. Son P. Nguyen
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  3. Uyen H. Pham
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  4. Thien Dinh Nguyen
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Corresponding author

Correspondence to Uyen H. Pham .

Editor information

Editors and Affiliations

  1. Computer Science Department, University of Texas at El Paso, El Paso, Texas, USA

    Vladik Kreinovich

  2. International College, Chiang Mai University, Chiang Mai, Thailand

    Songsak Sriboonchitta

  3. International College, Chiang Mai University, Chiang Mai, Thailand

    Nopasit Chakpitak

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Nguyen, D.T., Nguyen, S.P., Pham, U.H., Nguyen, T.D. (2018). A Calibration-Based Method in Computing Bayesian Posterior Distributions with Applications in Stock Market. In: Kreinovich, V., Sriboonchitta, S., Chakpitak, N. (eds) Predictive Econometrics and Big Data. TES 2018. Studies in Computational Intelligence, vol 753. Springer, Cham. https://doi.org/10.1007/978-3-319-70942-0_10

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  • DOI: https://doi.org/10.1007/978-3-319-70942-0_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70941-3

  • Online ISBN: 978-3-319-70942-0

  • eBook Packages: EngineeringEngineering (R0)

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