In my previous post “Bayesian Optimization”, I demonstrated the optimization procedure based on Bayesian method. However, Bayesian optimization has an issue of determining the initial samples in order ...

This is an implementation of Deutsch and Deutsch, "Latin hypercube sampling with multidimensional uniformity", Journal of Statistical Planning and Inference 142 (2012) , 763-772 ...

The design of sampling methods is crucial in digital soil mapping for soil organic carbon (SOC), as it directly affects prediction precision and reliability. While sampling methods based on ...

Abstract: Large deviations theory is a well-studied area which has shown to have numerous applications. The typical results, however, assume that the underlying random variables are either i.i.d. or ...

ABSTRACT: The aim of the paper is to present a newly developed approach for reliability-based design optimization. It is based on double loop framework where the outer loop of algorithm covers the ...

Just as its name implies, Monte Carlo Simulation is a mathematical technique that deals with randomness. This computational approach is used to model the probability of different outcomes in complex ...

ABSTRACT: In this paper, we are interested to find the most sensitive parameter, local and global stability of ovarian tumor growth model. For sensitivity analysis, we use Latin Hypercube Sampling ...