A univariate approximation at most probable point for higher-order reliability analysis

A Most Probable Point Based Method for Uncertainty Analysis

Uncertainty is inevitable at every stage of the life cycle development of a product. To make use of probabilistic information and to make reliable decisions by incorporating decision maker’s risk attitude under uncertainty, methods for propagating the effect of uncertainty are therefore needed. When designing complex systems, the efficiency of methods for uncertainty analysis becomes critical. ...

A univariate decomposition method for higher-order reliability analysis and design optimization

Efficient Approximation Algorithms for Point-set Diameter in Higher Dimensions

We study the problem of computing the diameter of a  set of $n$ points in $d$-dimensional Euclidean space for a fixed dimension $d$, and propose a new $(1+varepsilon)$-approximation algorithm with $O(n+ 1/varepsilon^{d-1})$ time and $O(n)$ space, where $0 < varepsilonleqslant 1$. We also show that the proposed algorithm can be modified to a $(1+O(varepsilon))$-approximation algorithm with $O(n+...

An iterative method for forecasting most probable point of stochastic demand

The demand forecasting is essential for all production and non-production systems. However, nowadays there are only few researches on this area. Most of researches somehow benefited from simulation in the conditions of demand uncertainty. But this paper presents an iterative method to find most probable stochastic demand point with normally distributed and independent variables of n-dime...

Structure Approximation of Most Probable Explanations in Bayesian Networks

Abstract Typically, when one discusses approximation algorithms for (NP-hard) problems (like TRAVELING SALESPERSON, VERTEX COVER, KNAPSACK), one refers to algorithms that return a solution whose value is (at least ideally) close to optimal; e.g., a tour with almost minimal length, a vertex cover of size just above minimal, or collection of objects that has close to maximal value. In contrast, o...