Bayesian inference on the intensity parameter of a mixed Poisson process considering a gamma prior density

Authors

DOI:

https://doi.org/10.19136/jobs.a11n30.6473

Keywords:

Bayesian statistics, Mixed Poisson process, Gamma distribution

Abstract

In this paper, considering a fixed time interval as the observation period, an inferential study is carried out to estimate the intensity parameter of a mixed Poisson process, under the assumption that the a priori density is gamma. The a posteriori density and Bayes estimators for the intensity parameter are calculated. Similarly, expressions are obtained for the predictive density for a new time between occurrences, the distribution of the number of events that occur in the observation interval and the joint density of the times between occurrences of events.

References

E. S. Keeping, (1962) Introduction to Statistical Inference, Van Nostrand.

E. Nájera Rangel, (2015) ¿Qué es la Estadística Bayesiana? Journal of Basic Sciences, 1(1). https://doi.org/10.19136/jobs.a1n1.1026.

J. C. Correa Morales, & C. J. Barrera Causil, (2018) Introducción a la estadística Bayesiana, Textos Académicos. DOI: https://doi.org/10.22430/9789585414242

J. Grandel, (1997) Mixed Poisson Processes, CRC Press. DOI: https://doi.org/10.1007/978-1-4899-3117-7

M. Pinsky & S. Karlin, (2010) An introduction to stochastic modeling, Elsevier. DOI: https://doi.org/10.1016/B978-0-12-381416-6.00001-0

Downloads

Published

2025-04-30

Issue

Section

Artículo científico

How to Cite

Pérez Reyes, L. G., Argáez Sosa, J., & Pantí Trejo, H. (2025). Bayesian inference on the intensity parameter of a mixed Poisson process considering a gamma prior density. JOURNAL OF BASIC SCIENCES, 11(30), 45-59. https://doi.org/10.19136/jobs.a11n30.6473