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Statistics with Matlab. Probability Distributions and Random Numbers

Statistics with Matlab. Probability Distributions and Random Numbers

          
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About the Book

Probability distributions are theoretical distributions based on assumptions about asource population. The distributions assign probability to the event that a randomvariable has a specific, discrete value, or falls within a specified range of continuous values. Statistics and Machine Learning Toolbox offers several ways to work with probabilitydistributions. -Use "Probability Distribution Objects" to fit a probability distributionobject to sample data, or to create a probability distribution object with specifiedparameter values. -Use "Probability Distribution Functions" to work with data input frommatrices, tables, and dataset arrays. -Use "Probability Distribution Apps and User Interfaces" to interactively fit, explore, and generate random numbers from probabilitydistributions. Available apps and user interfaces include: -The Distribution Fitter app -The Probability Distribution Function user interface -The Random Number Generation user interface (randtool) Pseudorandom numbers are generated by deterministic algorithms. They are "random" in the sense that, on average, they pass statistical tests regarding their distribution andcorrelation. They differ from true random numbers in that they are generated by analgorithm, rather than a truly random process.Random number generators (RNGs) like those in MATLAB are algorithms for generatingpseudorandom numbers with a specified distribution. Methods for generating pseudorandom numbers usually start with uniform randomnumbers, like the MATLAB rand function produces, but MATLAB produce random numbers from other distributions. This book develops the work with probability distribution and random numbers across examples using MATLAB. The more important topics in the book are de next: - Working with Probability Distributions - Continuous Distributions (Data) - Continuous Distributions (Statistics) - Discrete Distributions - Multivariate Distributions - Nonparametric Distributions - Flexible Distribution Families - Maximum Likelihood Estimation - Negative Loglikelihood Functions - Random Number Generation - Nonparametric and Empirical Probability Distributions - Fit Kernel Distribution Object to Data - Fit Kernel Distribution Using ksdensity - Fit Distributions to Grouped Data Using ksdensity - Fit a Nonparametric Distribution with Pareto Tails - Generate Random Numbers Using the TriangularDistribution - Model Data Using the Distribution Fitter App - Fit a Distribution Using the Distribution Fitter App - Custom Distributions Using the Distribution Fitter App - Explore the Random Number Generation UI - Compare Multiple Distribution Fits - Fit Probability Distribution Objects to Grouped Data - Multinomial Probability Distribution Objects - Multinomial Probability Distribution Functions - Generate Random Numbers Using Uniform Distribution Inversion - Represent Cauchy Distribution Using t Location-Scale - Generate Cauchy Random Numbers Using Student's t - Generate Correlated Data Using Rank Correlation - Gaussian Mixture Models - Create a Gaussian Mixture Model - Fit a Gaussian Mixture Model to Data - Simulate Data from a Gaussian Mixture Model - Copulas: Generate Correlated Samples - Generating Random Data - Random Number Generation Functions - Common Pseudorandom Number Generation Methods - Generating Quasi-Random Numbers - Generating Data Using Flexible Families of Distributions - Bayesian Linear Regression Using Hamiltonian MonteCarlo


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Product Details
  • ISBN-13: 9781979305297
  • Publisher: Createspace Independent Publishing Platform
  • Publisher Imprint: Createspace Independent Publishing Platform
  • Language: English
  • ISBN-10: 1979305293
  • Publisher Date: 16 Dec 2018
  • Binding: Paperback


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