• ## Prediction of volatility of S & P 500 index time series by Python random volatility (SV) model

Time：2021-10-5

Original link:http://tecdat.cn/?p=22546  Asset prices have volatility over time (variance of daily returns). In some periods, yields are highly variable, while in other periods they are very stable. The stochastic volatility model simulates this situation with a potential volatility variable, which is modeled as a stochastic process. The following model is similar to that described in […]

• ## Watermelon book Exercises – Chapter 07 – Bayesian classifier

Time：2021-9-24

Try and answer series: “watermelon book” – try and answer exercises of Zhou Zhihua’s machine learning Series catalog[chapter 01: introduction][chapter 02: model evaluation and selection][chapter 03: linear model][chapter 04: decision tree][chapter 05: neural networks][chapter 06: support vector machine] Chapter 07: Bayesian classifier Chapter 08: Integrated Learning Chapter 09: clustering Chapter 10: dimension reduction and metric […]

• ## Transfer to hair loss? Automatic super parameter search saves you with free computing resources!

Time：2021-9-22

In the field of artificial intelligence, algorithm engineers often need to optimize various parameters of the model to obtain better model effect after completing the network construction and preparing the training data in the process of training the neural network model. However, parameter adjustment is not simple. Behind it is often parameter debugging and effect […]

• ## Python implements Bayesian linear regression model with pymc3

Time：2021-8-27

Original link:http://tecdat.cn/?p=5263 In this paper, we will introduce regression modeling into Bayesian framework and use pymc3 MCMC library for reasoning. We will first review the multiple linear regression method of classical frequency theory. Then discuss how Bayesian considers linear regression. Bayesian linear regression with pymc3 In this section, we will make a statistical exampleclassicThe method […]

• ## Python Bayesian regression analysis of housing affordability data set

Time：2021-8-26

Original link:http://tecdat.cn/?p=11664  I want to study how to use pymc3 for linear regression within the Bayesian framework. Infer from the knowledge learned from the data. What is the Bayesian rule? In essence, we must combine what we already know with the facts of the world. Here is an example. Assuming the existence of this rare […]

• ## Bayesian quantile regression, lasso and adaptive lasso Bayesian quantile regression analysis are implemented in R language

Time：2021-8-25

Original link:http://tecdat.cn/?p=22702 abstract Bayesian regression quantile has attracted extensive attention in recent literature. This paper realizes Bayesian coefficient estimation and variable selection in regression quantile (RQ), Bayesian with lasso and adaptive lasso penalty. It also includes the further modeling functions of summarizing the results, drawing the path map, a posteriori histogram, autocorrelation map and drawing […]

• ## Block Gibbs Gibbs sampling Bayesian multiple linear regression in R language

Time：2021-8-23

Original link:http://tecdat.cn/?p=11617 Original source:Tuo end data tribal official account In this article, I will use Gibbs sampling of block for multiple linear regression to obtain the conditional posterior distribution required for Gibbs sampling of block. Then, the sampler is coded and tested with simulated data. Bayesian model Suppose we have a sample size of the […]

• ## Python utility, pyqt5 module, python realizes guessing each other’s gender according to Chinese name

Time：2021-8-19

preface: Using Bayesian formula, guess the other party’s gender according to the other party’s Chinese name. No more nonsense. Let’s start happily~ development tool Python version:3.6.4 Related modules: Pyqt5 module; And some Python built-in modules. Environment construction Install Python and add it to the environment variable. PIP can install the relevant modules required. Principle introduction […]

• ## 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 […]

• ## Five lines of code speed up the learning of scikit learn parameters by five times

Time：2021-6-5

By Michael ChauCompile VKSource: towards Data Science As we all know, scikit learn is a product that data scientists basically know. It provides dozens of easy-to-use machine learning algorithms. It also provides two ready-made technologies to solve the super parameter adjustment problem: grid search CV and random search cv. These two technologies are powerful ways […]

• ## R language Bayesian inference and MCMC: an example of metropolis Hastings sampling algorithm

Time：2021-6-1

Link to the original text:http://tecdat.cn/?p=21545 Example 1: exponential distribution sampling using MCMC The goal of any MCMC scheme is to generate samples from the “target” distribution. In this case, we will use an exponential distribution with an average of 1 as our target distribution. So we start by defining the target density: target = function(x){ […]

• ## R language Bayesian linear regression and multiple linear regression to build wage forecasting model

Time：2021-5-14

Link to the original text:http://tecdat.cn/?p=21641  Wage model In the field of labor economics, the study of income and wages provides insights from gender discrimination to higher education. In this paper, we will analyze cross-sectional wage data, in order to use Bayesian methods, such as BIC and Bayesian model, to build wage forecasting model in practice. […]