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Supervised Learning
Learn about supervised learning with MATLAB. Resources include videos, examples, and documentation.
Unsupervised Learning
Learn about Unsupervised Learning using MATLAB. Resources include videos, examples, and documentation covering supervised learning and other topics.
Cluster Analysis
Learn about Cluster Analysis using MATLAB. Resources include videos, examples, and documentation covering cluster analysis and other topics.
Nonlinear Regression
Learn about MATLAB support for nonlinear regression. Resources include examples, documentation, and code describing different nonlinear models.
Noninear Model
Learn about MATLAB support for nonlinear model. Resources include code examples, videos, and documentation describing different nonlinear models.
機械学習
"機械学習は、データに潜むパターンや規則性などを見つけ、それを予測に生かすための技術として現在大きな注目を集めている技術です。概要、参考例やビデオをご覧ください。
Linear Model
Learn about MATLAB support for linear models. Resources include code examples, documentation, and videos describing linear model and regression techniques.
Linear Regression
Learn about MATLAB support for linear regression. Resources include code examples, documentation, and videos describing linear model and regression techniques.
Régression Linéaire
Découvrez comment faire une régression linéaire avec MATLAB et ses outils : vidéos, exemples et documents disponibles
Monte Carlo Simulation
Learn how to perform Monte Carlo simulations in MATLAB and Simulink. Videos and examples show how to apply statistical uncertainties to a model and perform simulations in parallel.
Submitted: August 8, 2012
AdaBoost  
Learn about MATLAB support for AdaBoost. Resources include examples, documentation, and code describing different boosting algorithms.
Submitted: February 24, 2012
Regularization  
Learn about MATLAB support for regularization. Resources include examples, documentation, and code describing different regularization algorithms.
Submitted: February 8, 2012
Pharmacokinetic/Pharmacodynamic (PK/PD) Model  
Learn about MATLAB support for pharmacokinetic models. Resources include SimBiology models, examples, documentation, and code describing pharmacokinetic modeling.
Submitted: December 12, 2011
Cointegration  
Learn how to test for, analyze, and model cointegration in MATLAB. Resources include examples and documentation covering cointegration testing, modeling, and analysis including Engle-Granger and Johansen methods.
Submitted: November 7, 2011
GARCH Models  
Learn how to model GARCH processes in MATLAB. Resources include examples and documentation covering GJR, EGARCH, and GARCH models.
Submitted: November 7, 2011
MathWorks - Statistics Toolbox  
The Statistics Toolbox is an easy-to-use environment for analyzing historical data, modeling systems to predict their behavior, developing statistical algorithms, and learning and teaching statistics. Interactive GUI tools let you apply statistical methods easily and consistently, while the MATLAB language lets you easily create custom statistical methods and analyses. This combination gives you the freedom to access functions such as probability and ANOVA directly from the command line, or to use the interactive interfaces to learn and experiment with the Toolbox's built-in visualization and analysis tools.
Submitted: Jul 02, 1999
Machine Learning
Learn about MATLAB support for machine learning. Resources include examples, documentation, and code describing different machine learning algorithms.
Submitted: September 9, 2011
Smoothing
Learn about MATLAB support for smoothing. Resources include examples, documentation, and code describing different smoothing techniques.
Submitted: September 9, 2011
Random Number
Learn how to generate pseudorandom and quasi-random numbers in MATLAB. Resources include examples, documentation, and code describing random number generators.
Submitted: September 9, 2011
Statistics M-files  
User contributed statistics m-files from the MATLAB Central File Exchange.
Submitted: Apr 18, 2000
MATLAB, Statistics, and Linear Regression  
Introduction to MATLAB for Statistics. Includes some material on Variance and Covariance, and Linear regression.
Submitted: Aug 12, 2004
Advanced Statistics Toolbox  
Includes: Chi lack of fit test, Joint Confidence Region, Least Squares, Least Squares for Response Surfaces, Multivariable Response Regression and Eigenvalues, NonLinear Param estimation, Rational Approx to any data set.
Submitted: Jul 19, 1999
Statbox Toolbox  
A toolbox of statistical routines, including ordinal logistic regression, Poisson regression, nonlinear regression with sums of exponentials, maximum likelihood and REML for randomized block models, probability distributions, Gaussian quadrature and some other associated special functions and matrix operations.
Submitted: Jun 30, 1999
Statistical Computation: Archives of MATLAB Functions  
Archives on MATLAB functions. Packages include, regresssions, LAD, logit, factor analysis, distributions, statistics, strings/IO, date/time, and utility. Note that functions are minimally documented.
Submitted: Aug 12, 2004
Stat/Transfer - The Easiest Way to Move Data In and Out of MATLAB  
Stat/Transfer provides a seamless connection between MATLAB and leading statistical packages such as SAS, SPSS, Stata, S-Plus, Gauss, and LIMDEP, databases, such as Access, dBASE, and Paradox, and spreadsheets such as Excel, Quattro, and 1-2-3. It also intelliently handles delimited ASCII data. Available for 32-bit Windows, Linux, and popular Unix platforms.
Submitted: Aug 22, 2000
glmlab  
glmlab is a free MATLAB toolbox for analysing generalized linear models. glmlab can fit all types of generalized linear models, including (among others): multiple regression; log-linear models; logistic regression; and weighted regression. To achieve these tasks, it incorporates five error distributions and eight links function--plus the ability to add your own. It uses a graphical user interface and is very easy to learn, and even comes with an on-line manual! Some error analysis is also included, as well as a quick tutorial.
Submitted: Aug 31, 1999
Statistics Utilities by Peter J.Acklam  
Statistical functions, probability distributions, random numbers. Functions and examples.
Submitted: Aug 12, 2004
Clustering package  
A collection of m-files to do clustering. For now, only k-means clustering is implemented and a very slow agglomerative procedure. However, the support routines are quite useful.
Submitted: Jun 30, 1999
K Means Clustering Tutorial  
Simple tutorial on what is k means clustering, how the algorithm works, and numerical example of this code (in Matlab)and other resources in k means clustering
Submitted: Jul 06, 2005
Kernel Density Estimation Toolbox  
A MATLAB class with MEX routines for creating and manipulating non-parametric (kernel-based) density estimates. Supports quadratic, Gaussian and Laplacian (product) kernels of arbitrary dimension, several automatic bandwith selection methods, and uses KD-tree representations to enable fast approximate evaluation.
Submitted: Dec 10, 2003
Stochastic simulation using MATLAB  
A tutorial on stochastic simulation. The page demonstrates basic techniques for effective simulation and visualization of a number of random variables and random processes. From the table of contents: * random numbers from simple distributions; * basic random processes: random walks, Poisson processes; * queuing systems, birth-and-death processes, branching processes; * counting processes, renewal processes, renewal reward processes, on-off processes; * random trees, Waxman random network topology generator, branching Brownian motion in the plane; * aggregated teletraffic models: superposition of renewal processes, infinite source Poisson (M/G/Infinity) model, integrated sum of on-off processes
Submitted: Jan 08, 2006
Resampling Stats in MATLAB - Software for computer-intensive statistical resampling methods  
Resampling Stats in MATLAB is designed specifically for the "new statistics" of resampling including bootstrapping and permutation procedures. Resampling methods have become the "treatment of first choice" for confidence intervals, hypothesis testing, and assessing errors in estimates. Resampling Stats in MATLAB gives you the additional commands you need to do resampling easily in MATLAB, with numerous examples. It includes documentation on all resampling commands and pointers on using existing MATLAB commands in your resampling work.
Submitted: Apr 07, 2000
Stixbox: A Statistics Toolbox  
Stixbox, developed by Anders Holtsberg, is a statistics toolbox for MATLAB, Octave, and Matcom/Mideva.
Submitted: Feb 22, 2000
MCMC Methods for MLP and GP and Stuff  
MCMCstuff toolbox is a collection of Matlab functions for Bayesian inference with Markov chain Monte Carlo (MCMC) methods. Includes code and demonstrations for neural networks and Gaussian processes for classification and regression.
Submitted: Sep 30, 2006
MLR with Fit and CV Statistics  
The function mlr.m generates an MLR model fit and does 'leave one out' cross-validation of the model. Measures of R-squared, Adjusted R-squared, root-mean-square error of calibration (RMSEC), and root-mean-square error of cross-validation (RMSECV) are printed to the screen. The function also generates a table with the true and fitted values are along with the relative percent error (RPE) and upper and lower limits of an approximate 95% confidence interval on future observed values assuming normality.
Submitted: Nov 02, 1999
Spatial Statistics toolbox  
Contains procedures for quickly finding neighboring observations and for speedy estimation of several types of spatial autoregressions (e.g., SAR, CAR). A particular strength of the toolbox is its ability to handle large data sets. Traditionally, the need to evaluate the determinant of a matrix of order n made spatial statistics difficult to implement. Using sparse matrix capabilities and other techniques allows data sets of over 100,000 observations easily.
Submitted: Jun 30, 1999
MANI: Manifold Learning Demo  
This Matlab GUI provides a simple interface for visualizing and experimenting with various dimensionality reduction and manifold learning techniques. It is intended as an instruction tool and has proven useful to researchers interested in learning about dimensionality reduction. The algorithms available include: Principal Components Analysis (PCA), Multi-Dimensional Scaling (MDS), ISOMAP, Locally Linear Embedding (LLE), Hessian eigenmaps (HLLE), Laplacian eigenmaps, Diffusion maps, and Local Tangent Space Alignment (LTSA). The GUI provides several simple datasets such as the Swiss Roll for experimentation and comparison. Users can import their own datasets from text files or from the workspace.
Submitted: Jun 02, 2006
Correlation Pseudocolor Map Function  
The function corrmap.m displays a pseudocolor map of the correlation matrix for a input data set. This much would be easy, but it can also reorder the variables so that they are grouped by how correlated they are with each other. A modified k-nearest neighbor algorithm is used to reorder the variables. An example of its use is shown below. Here the data file plsdata was loaded into the MATLAB workspace. The plsdata file contains data from a slurry-fed ceramic melter process for solidifying the reprocessing wastes from nuclear fuels (yes, I know that this example is a bit unusual).
Submitted: Nov 02, 1999
Mixmod : cluster analysis and discriminant analysis sofware  
The MIXMOD (MIXture MODelling) software fits mixture models to a given data set with a density estimation, a clustering or a discriminant analysis purpose. A large variety of algorithms to estimate the mixture parameters are proposed (EM, Classification EM, Stochastic EM) and it is possible to combine them to lead to different strategies in order to get a sensible maximum of the likelihood (or complete-data likelihood) function. Moreover, different information criteria for choosing a parsimonious model (the number of mixture components, for instance), some of them favoring either a cluster analysis or a discriminant analysis view point, are included. Written in C++, MIXMOD is interfaced with MATLAB.
2006-06-22 : Mixmod 1.7.1 is available 2007-02-06 : Mixmod 2.0 is available : this new major release includes the treatment of qualitative data see : http://www-math.univ-fcomte.fr/mixmod/news.php

Submitted: Nov 29, 2005
GML RANSAC Matlab Toolbox  
GML RANSAC Matlab Toolbox addresses the problem of parametric model estimation. Toolbox is a set of MATLAB scripts, implementing RANSAC algorithm family, for robust estimation: *RANSAC *LMedS *LO-MSAC *MLESAC *MSAC *NAPSAC *R-RANSAC *ZHANGSAC
Submitted: Apr 03, 2006
Tutorials on Processing of Data using MATLAB  
Tutorials below aimed at freshman engineering students using Student Version 7.01 of MATLAB, i.e. with only the symbolic toolbox.
Submitted: Jun 03, 2005
Resources for K-Mean Clustering  
Aside from my tutorial (in Visual Basic Code or in MATLAB code), there are many books and journals or Internet resources discuss about K-mean clustering, your search must be depending on your application. Below are a few list that you may consider.
Submitted: Jun 15, 2005
Estimation of Distribution Algorithms  
MATEDA implements a number of Estimation of Distribution Algorithms (EDAs) commonly found in the literature. These programs are conceived to help in the initial validation of EDA approaches to different optimization problems. Current implementation includes EDAs for discrete and continuous problems. EDAs based on Bayesian and undirected graphical models have been included.
Submitted: Oct 13, 2005
chsone.m  
Chi-Square Significance Test.
Submitted: Jul 19, 1999
Spatial Statistics Toolbox for MATLAB 1.1  
Spatial Statistics Toolbox for MATLAB 1.1 includes code for simultaneous spatial autoregressions (SAR), conditional spatial autoregressions (CAR), and mixed regressive spatially autoregressive (MRSA) models, along with some additions.
Submitted: Apr 28, 2000
mad.m  
Median Absolute Distances from the sample median. Y = MAD(X) computes the robust estimator of scale MAD (Median Absolute Distances from the sample median) for the vector X.
Submitted: Aug 13, 1999
Data Description Toolbox (dd_tools)  
The data description toolbox wants to provide tools, classifiers and evaluation functions for the research of one-class classification (or data description)
Submitted: Sep 15, 2005
50-50 MANOVA with rotation testing  
The Matlab function ffmanova.m performs general linear modeling with multiple responses (MANCOVA). An overall p-value for each model term is calculated by the 50-50 MANOVA method, which handles collinear responses. Rotation testing is used to compute adjusted single response p-values according to familywise error rates and false discovery rates. Predictions, mean predictions and least squares means can also be calculated.
Submitted: Aug 21, 2000
RMTool: Random Matrix Calculator  
RMTool is a simple MATLAB Symbolic Toolbox based code for analytically predicting the eigenvalue distribution of a large class of complicated random matrices.
Submitted: Feb 16, 2006
Galton's Triangle experiment  
This is a graphical simulation of the famous Galton's triangle: a ball falls and meets nails arranged according to a triangular shape. Each time the ball meets a nail, it bounces to the right or the the left of it, according to a probability p=0.5, and independently of the previous stage.
Submitted: Jul 19, 1999
wmedian.m  
The function wmedian(data) computes the Hodges-Lehmann estimator W for the population central tendency.
Submitted: Jul 19, 1999
Data Mining in MATLAB  
Exploring data mining using MATLAB (and sometimes MATLAB Toolboxes).
Submitted: Jul 19, 2007
ARMADA Data Mining Tool  
An association rule data mining tool for experimentation and analysis. The associated files can be downloaded at the MATLAB Central File Exchange.
Submitted: Feb 13, 2003
Spatial Analysis 3D  
Spatial Analysis 3D is a user-friendly, graphical user interface (GUI) that allows statistical and visual manipulations of real and simulated three-dimensional spatial point patterns. Examples of the types of analyses performed include those derived from the Delaunay tessellation associated with such spatial point patterns, and those associated with the correlation of such point patterns, including autocorrelation analysis and its derived density recovery profile, as well as the related K, F, and G-functions. The stimulus for the development of Spatial Analysis 3D has been the study of neuronal positioning within the central nervous system, but many other applications in science, engineering, statistics and mathematics should benefit from this suite of programs. Spatial Analysis 3D is the project of a collaborative research effort between Drs. Benjamin Reese, Mary Raven, and Dan Lofgreen at the Unversity of California at Santa Barbara and Dr. Stephen Eglen at the University of Cambridge. It has been supported by a grant from the National Institute of Mental Health through the Neurotechnology Research, Development and Enhancement Program. It grew out of our efforts to quantify the regularity and simulate the patterning found in distributions of nerve cells across the retina, a structure in the central nervous system where uniformity in nerve cell spacing plays a critical role in retinal function.
Submitted: Nov 09, 2007
RanLip - universal nonuniform multivariate random variate generator  
RanLip is a method of generation of random variates with arbitrary Lipschitz-continuous densities, which works in the univariate and multivariate cases, in up to 5-6 variables. A Matlab toolbox is available, which includes the manual, examples and mex file.
Submitted: Dec 19, 2006
Process Simulation Software  
SimEvents is a process simulation software that helps understand resource requirements, timelines for complex large-scale missions, and the effects of arbitrary events on a mission plan.
Submitted: Mar 10, 2010
金融工学  
MATLAB® のプログラミング環境と予め組み込まれた金融工学算を使用して、金融の専門家は他のソフトウェアを使用した場合に比べ極めて短時間で定量的アプリケーションを世界中で開発しています。
統計解析MATLAB入門  
統計解析・可視化をMATLABで簡単に実現する手法をご紹介するオンデマンドセミナーです。



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