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Bayesian Estimation

In: Algorithms with JULIA

Author

Listed:
  • Clemens Heitzinger

    (Technische Universität Wien, Center for Artificial Intelligence and Machine Learning (CAIML) and Department of Mathematics and Geoinformation)

Abstract

Frequentist and Bayesian statistics and inference differ in their fundamental assumptions on the nature of probabilities and models. After a short discussion of the differences,we use the ideas of Bayesian inference to determine model parameters. The motivation for these considerations is the fact that models usually contain parameters that are unknown and often cannot be measured or determine directly. Thus they must be estimated by comparing the model to data. In this chapter, the Bayesian approach to the estimation of model parameters is developed, implemented, and applied to an example.

Suggested Citation

  • Clemens Heitzinger, 2022. "Bayesian Estimation," Springer Books, in: Algorithms with JULIA, chapter 0, pages 397-431, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-16560-3_14
    DOI: 10.1007/978-3-031-16560-3_14
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