Matlab Distributions
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This document discusses probability distributions and provides an overview of the types of distributions and functions supported in the Statistics and Machine Learning Toolbox. It describes probability distributions as identifying the probability of values a random variable can take. It lists several common distributions like the binomial, normal, and lognormal distributions. It then summarizes the different types of functions available for parametric estimation, nonparametric estimation, inverse cumulative distribution functions, distribution statistics, distribution fitting, negative log-likelihood, and random number generation.
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The document discusses visualizing data in Matlab. It describes how the plot function can be used to create graphs with different parameters. It also explains how to create animations by saving multiple frames from figures using getframe and playing them back with movie. An example is provided to generate an animation by plotting the FFT of an identity matrix over increasing sizes and saving each frame.
Matlab Working With Images
The document discusses different types of images in Matlab including binary, grayscale, indexed, and RGB images. It also summarizes commands to convert between image types such as converting grayscale to indexed or truecolor to binary. Finally, it provides examples of how to view images, measure pixel values and distances, and crop images using the imtool command.
Matlab: Non Linear Methods
This document discusses nonlinear curve fitting in Matlab. It describes how the Curve Fitting Toolbox uses nonlinear least squares to fit nonlinear models to data, such as Gaussians, ratios of polynomials, and power functions. The process of fitting a surface to data involves opening the fitting tool, selecting and importing data, refining the fit, removing outliers, selecting validation data, and exploring and customizing plots. Various steps for fitting, viewing residuals and fits, and analyzing the fit are outlined. The document recommends visiting additional self-help tutorials on the website.
Matlab: Procedures And Functions
Script files, also called M-files, make MATLAB programming more efficient than entering individual commands. M-files are text files that contain MATLAB commands to perform specific tasks. They can be created and edited in any plain text editor or the built-in MATLAB editor. User defined functions are a special type of M-file that take inputs and produce outputs, with the function name and arguments specified in the first line. Strings in MATLAB are matrices of character elements that can be manipulated using various functions like converting between data types and extracting substrings.
Matlab: Programming Environment
This document provides an overview of the MATLAB programming environment and basic functions. It describes the main components of the MATLAB interface including the toolbar, file manager, command window, workspace, and command history. It also explains how to access help documentation, use the function browser to search for commands, define basic functions and vectors, and clear variables from the workspace.
Matlab: Regression
Regression analysis models the relationship between variables, including dependent and independent variables. Linear regression models take forms like straight lines, polynomials, trigonometric, and interaction terms. Multiple linear regression is useful for understanding variable effects, predicting values, and dealing with multicollinearity using methods like ridge regression, partial least squares, and stepwise regression. Nonlinear and generalized linear models also describe nonlinear relationships. Multivariate regression involves multiple response variables.
Matlab: Saving And Publishing
This document discusses saving and publishing in Matlab. It describes how to save variables and images using commands like save and imsave. It also explains how to publish animations by converting MATLAB movie files to AVI format using the movie2avi function. This involves creating an AVI file object with avifile, then capturing a sequence of graphs using addframe and putting them into the AVI file.
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Matlab Distributions
- 2. Probability Distributions Wikipedia: A probability distribution identifies either the probability of each value of an unidentified random variable (when the variable is discrete), or the probability of the value falling within a particular interval (when the variable is continuous). The probability distribution describes the range of possible values that a random variable can attain and the probability that the value of the random variable is within any (measurable) subset of that range.
- 3. Types of supported distributions pdf — Probability density functions cdf — Cumulative distribution functions inv — Inverse cumulative distribution functions stat — Distribution statistics functions fit — Distribution fitting functions like — Negative log-likelihood functions rnd — Random number generators
- 13. Extreme Value
- 14. Distribution
- 15. F Distribution
- 19. Generalized Extreme Value Distribution
- 22. Pearson System
- 34. n = 10; % Number of trials
- 35. k = 0:n; % Outcomes
- 36. m = binopdf(k,n,p); % Probability mass vector
- 37. bar(k,m) % Visualize the probability distribution
- 41. MPG = cars.MPG;
- 42. hist(MPG)
- 45. plot(x,f);
- 47. Cumulative Distribution Functions Parametric Estimation Nonparametric Estimation
- 48. Inverse Cumulative Distribution Functions Each function in this family represents a parametric family of distributions. Input arguments are arrays of cumulative probabilities between 0 and 1 followed by a list of parameter values specifying a particular member of the distribution family.
- 49. Inverse Cumulative Distribution Functions The expinv function can be used to compute inverses of exponential cumulative probabilities:
- 50. Distribution Statistics Functions Each function in this family represents a parametric family of distributions. Input arguments are lists of parameter values specifying a particular member of the distribution family. Functions return the mean and variance of the distribution, as a function of the parameters.
- 52. b = 0.5:0.1:3;
- 54. M = wblstat(A,B);
- 56. Distribution Fitting Functions Fitting Supported Distributions Fitting Piecewise Distributions
- 57. Negative Log-Likelihood Functions Each function in this family represents a parametric family of distributions. Input arguments are lists of parameter values specifying a particular member of the distribution family followed by an array of data. Functions return the negative log-likelihood of the parameters, given the data.
- 58. Random Number Generators Each RNG represents a parametric family of distributions. Input arguments are lists of parameter values specifying a particular member of the distribution family followed by the dimensions of an array. RNGs return random numbers from the specified distribution in an array of the specified dimensions.
- 59. Visit more self help tutorials Pick a tutorial of your choice and browse through it at your own pace. The tutorials section is free, self-guiding and will not involve any additional support. Visit us at www.dataminingtools.net