Bayesian New Two-Sided Group Chain Sampling Plan for Poisson Distribution with Gamma Prior
Abstract
Acceptance sampling is a vital approach used to determine whether a lot should be accepted or rejected based on a random sample drawn from that lot. This study introduce a Bayesian New Two-Sided Group Chain Sampling Plan (BNTSGChSP) that incorporates various combinations of design parameters. In this framework, inspections are conducted based on both preceding and succeeding lots. We model the acceptance probability (AP) of a lot using Poisson distribution for nonconforming and conforming products, with a Gamma distribution serving as an appropriate prior for the Poisson model. Our analysis identifies the inflection points for specified combinations of design parameters. The findings suggest that the BNTSGChSP offers a superior alternative to existing sampling plans for industrial practitioners.
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Copyright (c) 2024 Waqar Hafeez, Nazrina Aziz
This work is licensed under a Creative Commons Attribution 4.0 International License.
