• ## Monte Carlo integration

Time：2021-7-7

By Cory maklinCompile VKSource: towards Data Science Usually, we can’t solve the integral analytically, we must use other methods, including Monte Carlo integral. As you may remember, the integral of a function can be interpreted as the area under the curve of the function. The working principle of Monte Carlo integration is to calculate a […]

• ## R language uses metropolis hasting sampling algorithm for logistic regression

Time：2021-6-10

Link to the original text: http://tecdat.cn/?p=6761 In logistic regression, we use the binary dependent variable y_ I regress to covariate X_ I go up. The following code uses metropolis sampling to explore beta_ 1 and beta_ 2 to covariate Xi. Define exit and fractional logarithm link function Logit < – function (x) {log (x / […]

• ## R language and Stan, jags: using rstan, rjag to establish Bayesian multiple linear regression to predict election data

Time：2021-4-28

Link to the original text:http://tecdat.cn/?p=21978  This paper will introduce how to use rstan and rjags to do Bayesian regression analysis in R. there are many packages in r that can be used to do Bayesian regression analysis, such as the earliest (and most references and examples) r2winbugs package. This package will call WinBUGS software to […]

• ## R language uses metropolis – Logistic regression with hasting sampling algorithm

Time：2021-4-20

Link to the original text:http://tecdat.cn/?p=6761 In logistic regression, we use the binary dependent variable y_ I regress to covariate X_ I go up. The following code uses metropolis sampling to explore beta_ 1 and beta_ 2 to covariate Xi. Define exit and fractional logarithm link function logit <- function ( x ){ log ( x […]

• ## Bayesian simple linear regression simulation analysis of R language Gibbs sampling

Time：2021-4-17

Link to the original text:http://tecdat.cn/?p=4612 Many introductions of Bayesian analysis use relatively simple teaching examples (for example, the inference of success probability based on Bernoulli data). Although this is a good introduction to Bayesian principles, it is not direct to extend these principles to regression. This article will outline how these principles can be extended […]