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Bayesian Approach for GLM

In: Generalized Linear Models and Extensions

Author

Listed:
  • M. Ataharul Islam

    (University of Dhaka, ISRT)

  • Soma Chowdhury Biswas

    (University of Chittagong, Department of Statistics)

Abstract

The Bayesian approach for GLM is discussed taking into account probability statements about parameters for any given set of data. In this chapter the assumptions are made on the model parameters in the form of prior distribution. In Bayesian generalized linear models’ framework, we have considered models for binary, multinomial, count data multivariate responses. This chapter includes a section on big data Bayesian GLM to make the users familiar with the increasingly useful Bayesian approaches to analyse the problems emerging from big data analytics.

Suggested Citation

  • M. Ataharul Islam & Soma Chowdhury Biswas, 2025. "Bayesian Approach for GLM," Springer Books, in: Generalized Linear Models and Extensions, chapter 0, pages 199-211, Springer.
    • Handle: RePEc:spr:sprchp:978-981-96-4726-2_11
    DOI: 10.1007/978-981-96-4726-2_11
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