Monte Carlo based stochastic approach for first order nonlinear ODE systems

Stochastic Methods: Monte Carlo Approach

Fdi in Nonlinear Stochastic Systems Using Adaptive Monte Carlo Filters and Likelihood Ratio Approach

In this paper, a new method for solving the fault detection and isolation (FDI) problem in general nonlinear stochastic systems is proposed. The proposed method is based on adaptive Monte Carlo filter and likelihood ratio approach. The simulation results on a highly nonlinear system are provided which demonstrate the effectiveness of the proposed method.

Nonlinearly Weighted First-order Regression for Denoising Monte Carlo Renderings

We address the problem of denoising Monte Carlo renderings by studying existing approaches and proposing a new algorithm that yields state-of-the-art performance on a wide range of scenes. We analyze existing approaches from a theoretical and empirical point of view, relating the strengths and limitations of their corresponding components with an emphasis on production requirements. The observa...

Hybrid, Multiscale Monte Carlo Algorithm for Simulating Stochastic Systems

INTRODUCTION Accurately relating the physiological outcome of a cell to the molecular events, based on an in silico analysis of intracellular networks, requires not only a precise knowledge of the network, but also an appropriate simulation technique. Traditionally, the methods used to temporally evolve intracellular networks have been deterministic in nature. However, deterministic methods can...

Nonlinear Stochastic Optimization by the Monte-Carlo Method

Methods for solving stochastic optimization problems by Monte-Carlo simulation are considered. The stoping and accuracy of the solutions is treated in a statistical manner, testing the hypothesis of optimality according to statistical criteria. A rule for adjusting the Monte-Carlo sample size is introduced to ensure the convergence and to find the solution of the stochastic optimization problem...