Improving Computer Experiment Designs with Two-Type Marked Point Processes

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DOI: https://doi.org/10.28924/2291-8639-23-2025-82
Published: Mar 31, 2025
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Hichem Elmossaoui, Ahmed Ait Ameur, Nadia Oukid, Improving Computer Experiment Designs with Two-Type Marked Point Processes, Int. J. Anal. Appl., 23 (2025), 82.

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Hichem Elmossaoui, Ahmed Ait Ameur, Nadia Oukid

Abstract

This paper presents an alternative approach to constructing computer experiment designs based on stochastic process theory, specifically marked point processes with two types. The originality of this method lies in the use of Markov Chain Monte Carlo (MCMC) techniques, combined with the Metropolis-Hastings algorithm and a new dynamic called local shift dynamics. These tools enable the generation of highly flexible and adaptable experimental designs, which can be tailored to a variety of specific objectives according to experimental needs. Special attention has been given to analyzing the convergence of the Markov chain, thus ensuring the robustness and efficiency of the results obtained. Additionally, a comparative study was conducted to position our method relative to other existing computer designs. This comparison highlights the advantages and disadvantages of our approach in terms of modularity and performance.

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