Particle filtering is an effective sequential Monte Carlo approach to solve the recursive Bayesian filtering problem in non-linear and non-Gaussian systems. The algorithm is base...
Recently, there is a growing interest in working with tree-structured data in different applications and domains such as computational biology and natural language processing. Mor...
— We extend the Evolution Strategy with Covariance Matrix Adaptation (CMA-ES) by collaborative concepts from Particle Swarm Optimization (PSO). The proposed Particle Swarm CMA-ES...
A method is presented that allows one to exactly determine all the characteristics of a PSO’s sampling distribution and explain how it changes over time, in the presence stochas...
Point estimates of the parameters in real world models convey valuable information about the actual system. However, parameter comparisons and/or statistical inference requires de...