Application of the Bayesian Method in Statistical Modeling, 2nd Edition

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Probability and Statistics".

Deadline for manuscript submissions: 10 April 2025 | Viewed by 12

Special Issue Editor


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Guest Editor
Department of Educational Leadership, Research, and School Improvement, University of West Georgia, Carrollton, GA 30118, USA
Interests: multivariate statistics; latent variable modeling; estimation methods; latent class analysis; latent profile analysis; factor analysis; structural equation modeling; cluster analysis
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Special Issue Information

Dear Colleagues,

Bayesian statistics is a theory in the field of statistics based on the Bayesian interpretation of probability, where probability expresses a degree of belief in an event. Bayesian statistical methods use Bayes' theorem to compute and update probabilities after obtaining new data. Named after Thomas Bayes, Bayes' theorem (1973) describes the conditional probability of an event based on data as well as prior information or beliefs about the event or conditions related to the event. This approach differs from other interpretations of probability, such as the frequentist interpretation, which views probability as the limit of the relative frequency of an event after many trials. During much of the 20th century, many statisticians viewed Bayesian methods unfavourably due primarily to practical considerations. Bayesian methods required much computation to complete, and the most widely used methods during the century relied on the frequentist interpretation. However, with the advent of powerful computers and new algorithms, such as Markov chain Monte Carlo, Bayesian methods have seen increasing use within statistics in the 21st century.

This Special Issue aims to raise awareness of the availability and applicability of Bayesian analyses. It includes a collection of theoretical and applied studies using Bayesian statistics and provides information on statistical software that allows the use of Bayesian estimation methods.

Dr. Diana Mindrila
Guest Editor

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Keywords

  • Bayesian analysis
  • Bayesian estimation
  • statistics
  • probability

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