Asymptotic equivalence between frequentist and Bayesian prediction limits for the Poisson distribution

This paper develops Bayesian prediction intervals for the minimum of any specified number of future measurements from a Gumbel distribution based on previous observations. The need for such intervals arises in the analysis of data from outlet side feeder pipes at Ontario nuclear power plants. The issue is how to best use these measurements in order to arrive at a statistically sound conclusion concerning the minimum thickness of all remaining uninspected pipes, in particular with what confidence can it be asserted that the remaining wall thicknesses are above an acceptable minimum to ensure a sufficiently high thickness up to the end of the next operating interval. The result gives a probability measure of the potential benefit of performing additional inspections when considered against the additional radiation exposure and the cost of performing additional inspections. Previously, this problem was approached by adapting a classical prediction interval that was originally derived for normal data. Here we examine both a hybrid Bayesian method that combines Bayesian ideas with maximum likelihood and also a full Bayesian approach using Markov Chain Monte Carlo. We show that the latter gives larger lower prediction limits and therefore more margin to fitness for service.

Download Full-text

Bayesian and frequentist prediction limits for the Poisson distribution

Communication in Statistics- Theory and Methods ◽

10.1080/03610926.2017.1373814 ◽

2017 ◽

Vol 47 (17) ◽

pp. 4254-4271 ◽

Cited By ~ 2

Author(s):

Valbona Bejleri ◽

Balgobin Nandram

Keyword(s):

Poisson Distribution ◽

Prediction Limits

Download Full-text

ULTIMATE THEOREMS OF CONVERGENCE TO POISSON DISTRIBUTION

RSUH/RGGU Bulletin. Series Information Science. Information Security. Mathematics ◽

10.28995/2686-679x-2018-1-94-110 ◽

2018 ◽

pp. 94-110

Author(s):

Shakir K. Formanov ◽

◽

Bainoza B. Khusainova ◽

Keyword(s):

Poisson Distribution

Download Full-text

Procedure and Tables for Construction and Selection of Modified Chain Sampling Plan (MChSP-1) with Zero � Inflated Poisson Distribution

Journal of Computer and Mathematical Sciences ◽

10.29055/jcms/975 ◽

2019 ◽

Vol 10 (1) ◽

pp. 33-42

Author(s):

M. Latha ◽

A. Palanisamy

Keyword(s):

Poisson Distribution ◽

Sampling Plan ◽

Selection Of

Download Full-text

Performance Comparison of Web Backend And Database: A Case Study Of Node.JS, Golang and MySQL, MongoDB

Recent Advances in Computer Science and Communications ◽

10.2174/2666255813666191219104133 ◽

2019 ◽

Vol 13 ◽

Author(s):

Faried Effendy ◽

Taufik ◽

Bramantyo Adhilaksono

Keyword(s):

Response Time ◽

Open Source ◽

Poisson Distribution ◽

Mobile Application ◽

Web Applications ◽

Performance Comparison ◽

Memory Usage ◽

Data Traffic ◽

Cpu Utilization

: Substantial research has been conducted to compare web servers or to compare databases, but very limited research combines the two. Node.js and Golang (Go) are popular platforms for both web and mobile application back-ends, whereas MySQL and Go are among the best open source databases with different characters. Using MySQL and MongoDB as databases, this study aims to compare the performance of Go and Node.js as web applications back-end regarding response time, CPU utilization, and memory usage. To simulate the actual web server workload, the flow of data traffic on the server follows the Poisson distribution. The result shows that the combination of Go and MySQL is superior in CPU utilization and memory usage, while the Node.js and MySQL combination is superior in response time.

Download Full-text