Nonlinear and nonnormal filters using Monte Carlo methods

Nonlinear and Nonnormal Filter Using Importance Sampling: Antithetic Monte Carlo Integration

In this paper, the importance sampling filter proposed by Mariano and Tanizaki (1995), Tanizaki (1996), Tanizaki and Mariano (1994) is extended using the antithetic Monte Carlo method to reduce the simulation errors. By Monte Carlo studies, the importance sampling filter with the antithetic Monte Carlo method is compared with the importance sampling filter without the antithetic Monte Carlo met...

Monte Carlo and quasi-Monte Carlo methods

Monte Carlo is one of the most versatile and widely used numerical methods. Its convergence rate, O(N~^), is independent of dimension, which shows Monte Carlo to be very robust but also slow. This article presents an introduction to Monte Carlo methods for integration problems, including convergence theory, sampling methods and variance reduction techniques. Accelerated convergence for Monte Ca...

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.

Inverse Kinematics Using Sequential Monte Carlo Methods

In this paper we propose an original approach to solve the Inverse Kinematics problem. Our framework is based on Sequential Monte Carlo Methods and has the advantage to avoid the classical pitfalls of numerical inversion methods since only direct calculations are required. The resulting algorithm accepts arbitrary constraints and exhibits linear complexity with respect to the number of degrees ...

Pricing Options Using Monte Carlo Methods