A second-order iterated smoothing algorithm

A second-order iterated smoothing algorithm

Simulation-based inference for partially observed stochastic dynamic models is currently receiving much attention due to the fact that direct computation of the likelihood is not possible in many practical situations. Iterated filtering methodologies enable maximization of the likelihood function using simulation-based sequential Monte Carlo filters. Doucet et al. (2013) developed an approximat...

Supplement to “A second-order iterated smoothing algorithm”

A Second-Order Perceptron Algorithm

Kernel-based linear-threshold algorithms, such as support vector machines and Perceptron-like algorithms, are among the best available techniques for solving pattern classification problems. In this paper, we describe an extension of the classical Perceptron algorithm, called second-order Perceptron, and analyze its performance within the mistake bound model of on-line learning. The bound achie...

Enhanced Second Order Algorithm

Second order algorithms belong to the class of repetitive heuristic techniques, where a greedy heuristic is applied to different subsets of the space of feasible solutions of a problem. The algorithm establishes a sequence of self-contained solutions subspaces, defining an iterative process that guides a subordinate greedy heuristic through those subspaces. This steepest descent process establi...

A New Second-order Strapdown Attitude Algorithm

Typically coning motion has been used as a standard input to assess the performance of the strapdown attitude algorithms. Traditional coning algorithms are based on the Bortz’s first-order rotation vector equation. The algorithm error is composed of two parts: drift error and approximation error. For the three (or more)-sample coning algorithms, calculation indicates that approximation error is...