Search Results for "parametric"

...The toolbox offers advanced analysis methods of MEG, EEG, and invasive electrophysiological data, such as time-frequency analysis, source reconstruction using dipoles, distributed sources and beamformers and non-parametric statistical testing. It supports the data formats of all major MEG systems (CTF, Neuromag/Elekta/Megin, BTi/4D, Yokogawa/Ricoh, FieldLine) and of most popular EEG systems, and new formats can be added easily. FieldTrip contains high-level functions that you can use to construct your own analysis protocols in MATLAB. Furthermore, it easily allows methods researchers to incorporate new methods for EEG/MEG analysis.

...Its primary focus in on the interpolation / regression technique known as kriging, which is very closely related to Splines and Radial Basis Functions, and can be interpreted as a non-parametric Bayesian method using a Gaussian Process (GP) prior. The STK also provides tools for the sequential and non-sequential design of experiments. Even though it is, currently, mostly geared towards the Design and Analysis of Computer Experiments (DACE), the STK can be useful for other applications areas (such as Geostatistics, Machine Learning, Non-parametric Regression, etc.).

...If you have any questions, please contact Shengmin Zhou(email: 2495077522@qq.com or 220180304@seu.edu.cn). If you want to download this project and use it, please cite the paper: Zhou, S., Li, B. & Nie, H. Parametric fitting and morphometric analysis of 3D open curves based on discrete cosine transform. Zoomorphology (2021). https://doi.org/10.1007/s00435-021-00520-w

Vibrato Toolbox: Parametric Analysis of Musical Vibrato from High-Precision Magnitude/Frequency Tracking Mingfeng Zhang ECE, Univ. of Rochester Part I Analysis of individual music tones This MATLAB toolbox provides signal analysis tools for time-frequency analysis and for parametric analysis of musical vibrato Part II Analysis of multiple music tones or multiple harmonic partials This toolbox allows you to explore a group of audio files, and obtains its related parameters of musical vibrato. ...

This Matlab toolbox runs a GLM on graph theoretic network properties in brain networks. The GLM accepts continuous & categorical between-participant predictors and categorical within-participant predictors. Significance is determined via non-parametric permutation tests. The toolbox allows testing of both fully connected and thresholded networks (based on a range of thresholds). The toolbox also provides a data processing path for resting state fMRI data. Several options for partialing nuisance signals are available, including local and total white matter signal (Jo et al., 2013), calculation of Saad et al. (2013)'s GCOR, and the use of Chen et al. (2012) GNI method to determine whether global signal partialing is needed. ...

...The CDTB contains several functions from the following categories. 1. graph generators; 2. clustering algorithms; 2. cluster number selection functions; 4. clustering evaluation functions. Furthermore, CDTB is designed in a parametric manner so that the user can add his own functions and extensions. The CDTB can be used in at least three ways. The user can employ the functions from the MATLAB command line; or he can write his own code, incorporating the CDTB functions; or he can use the Graphical User Interface (GUI) which automates the community detection and includes some data visualization options.

...Its inferential capabilities can be considered surpassed (at best). Actually the parameter estimation methods were already far from the state-of-art when the project began in 2007 (!). The considered implemented parametric and non-parametric Monte Carlo likelihood methods were chosen for their ability to treat both one-dimensional and multivariate SDE systems, although the quality of the inferential results can't match those obtained using more advanced techniques. Nevertheless the toolbox capabilities to simulate numerical solutions of SDE systems are still valid and can serve as a useful starting point to those willing to simulate stochastic dynamical models easily.