gamma distribution in r

Learn about gamma distribution in r, we have the largest and most updated gamma distribution in r information on

"Mathematics in machine learning" generalized inverse Gaussian distribution and its special cases

IntroductionThe generalized inverse Gaussian is a kind of rich probability distribution, and its parameters are derived from some classical useful distributions when the parameter is a certain value.Generalized inverse Gaussian distribution

Data types and data distribution

1. Dissociation data and discrete distributionDissociation data is usually data that can only be represented by integers. For example, the number of people in a province, the number of planets in a unit volume in the universe, etc.1.1 The discrete

Use C ++ tr1 to generate a random number

Use C ++ tr1 to generate a random number Original: [Original ENGLISH]Translation: Orbit)Introduction This article describes how to use the random number generation function provided by C ++ tr1 (C ++ Standard Committee Technical Report 1 ).In

Using Scikit-learn to study spectral clustering

In the summary of the principle of spectral clustering (spectral clustering), we summarize the principle of spectral clustering. Here we make a summary of the use of spectral clustering in Scikit-learn.1. Scikit-learn Spectral Clustering OverviewIn

A brief introduction to several statistical distributions in R

There are many statistical distributions, which are basically described in R. Due to their limited capabilities, we have selected a few common, more important, and simple descriptions of each distribution definition, formula, and presentation in

R language Learning Note-probability function

In the R language, different distributions can be generated for experimentation and learning.In R, the probability function is shaped like ①:where the first letter denotes one aspect of the distribution that it refers to:D = density function

R Language Data Analysis series four

R Language Data Analysis series four --by Comaple.zhangWhen it comes to statistical analysis, we can't leave random variables, so-called random variables are mathematical models that mathematicians build to better fit the real-world data. With her,

R Language Basics

creation and manipulation of vectors C () create vector length () mode () to determine the type of vector rbind () by row combination Vector Cbind () by column

Markov decision-making process

  Markov characteristics: The state of the next time point is only related to the current time point and is irrelevant to the previous time point. That is, the State Information contains all historical information.Markov reward process, $ $: $ S $

R in Action reading notes (1)--fifth: Advanced data management

5.2.1 Mathematical Functions Function Describe ABS (x) Absolute sqrt (x) Square root Ceiling (x) Smallest integer not less than X Floor (x) Largest

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