A MCMC-type simple probabilistic approach for determining optimal progressive censoring schemes

TL;DR

This paper introduces a probabilistic method for identifying optimal progressive censoring schemes, especially effective when the solution space is large, validated against exhaustive search methods.

Contribution

It proposes a novel probabilistic framework for selecting optimal censoring schemes, improving efficiency over exhaustive methods in large solution spaces.

Findings

The probabilistic approach closely matches exhaustive search results.

The method is computationally efficient for large solution sets.

Validation shows high accuracy in scheme selection.

Abstract

We present here a simple probabilistic approach for determining an optimal progressive censoring scheme by defining a probability structure on the set of feasible solutions. Given an initial solution, the new updated solution is computed within the probabilistic structure. This approach will be especially useful when the cardinality of the set of feasible solutions is large. The validation of the proposed approach is demonstrated by comparing the optimal scheme with these obtained by exhaustive numerical search.