Bayesian Estimation

Clemens Heitzinger ()
Additional contact information
Clemens Heitzinger: Technische Universität Wien, Center for Artificial Intelligence and Machine Learning (CAIML) and Department of Mathematics and Geoinformation

Chapter Chapter 14 in Algorithms with JULIA, 2022, pp 397-431 from Springer

Abstract: 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.

Date: 2022
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-16560-3_14

Ordering information: This item can be ordered from
http://www.springer.com/9783031165603

DOI: 10.1007/978-3-031-16560-3_14

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().