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 ...

Monte Carlo methods have become important in analysis of nonlinear inverse problems where no analytical expression for the forward relation between data and model parameters is available, and where linearization is unsuccessful. In such cases a direct mathematical treatment is impossible, but the forward relation materializes itself as an algorithm allowing data to be calculated for any given m...

The full application of Bayesian inference to inverse problems requires exploration of a posterior distribution that typically does not possess a standard form. In this context, Markov chain Monte Carlo (MCMC) methods are often used. These methods require many evaluations of a computationally intensive forward model to produce the equivalent of one independent sample from the posterior. We cons...

A Monte Carlo based inverse treatment planning system (MCI) has been developed which combines arguably the most accurate dose calculation method (Monte Carlo particle transport) with a 'guaranteed' optimization method (simulated annealing). A distribution of photons is specified in the tumour volume; they are transported using an adjoint calculation method to outside the patient surface to buil...

The problem of parameter estimation in chaotic noise is considered in this paper. Based on the inherently deterministic nature of a chaotic signal — the short term predictability, a novel estimation approach called minimum nonlinear prediction error (M) technique is proposed. The parameters of a signal can be accurately estimated by minimizing the nonlinear prediction error of the output of an ...