Dynamics of Langevin Simulation

This chapter [of a supplement to Prog. Theo. Phys.] reviews numerical simulations of quantum field theories based on stochastic quantization and the Langevin equation. The topics discussed include renormalization of finite step-size algorithms, Fourier acceleration, and the relation of the Langevin equation to hybrid stochastic algorithms and hybrid Monte Carlo.Comment: 20 p

Machine learning to analyze single-case data : a proof of concept

Visual analysis is the most commonly used method for interpreting data from singlecase designs, but levels of interrater agreement remain a concern. Although structured aids to visual analysis such as the dual-criteria (DC) method may increase interrater agreement, the accuracy of the analyses may still benefit from improvements. Thus, the purpose of our study was to (a) examine correspondence between visual analysis and models derived from different machine learning algorithms, and (b) compare the accuracy, Type I error rate and power of each of our models with those produced by the DC method. We trained our models on a previously published dataset and then conducted analyses on both nonsimulated and simulated graphs. All our models derived from machine learning algorithms matched the interpretation of the visual analysts more frequently than the DC method. Furthermore, the machine learning algorithms outperformed the DC method on accuracy, Type I error rate, and power. Our results support the somewhat unorthodox proposition that behavior analysts may use machine learning algorithms to supplement their visual analysis of single-case data, but more research is needed to examine the potential benefits and drawbacks of such an approach

Supplement to the Paper "Interative plug-in algorithms for SEMIFAR models - definition, convergence and asymptotic properties": Detailed Simulation Results

This paper is written as a supplement to our paper Iterative plug-in algorithms for SEMIFAR models-definition, convergence and asymptotic properties (Beran and Feng, 2001). The purpose of this supplement is to report the detailed simulation results, because it is impossible to include all of them in the original paper. Some technical details about programming are also described. For the theoretical and methodical background we refer the reader to the original paper.SEMIFAR models, data-driven algorithms, simulation results