Tag：Bayes

Time：2021105
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 […]

Time：2021924
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 […]

Time：2021922
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 […]

Time：2021827
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 […]

Time：2021826
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 […]

Time：2021825
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 […]

Time：2021823
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 […]

Time：2021819
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 builtin modules. Environment construction Install Python and add it to the environment variable. PIP can install the relevant modules required. Principle introduction […]

Time：202177
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 […]

Time：202165
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 easytouse machine learning algorithms. It also provides two readymade technologies to solve the super parameter adjustment problem: grid search CV and random search cv. These two technologies are powerful ways […]

Time：202161
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){ […]

Time：2021514
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 crosssectional wage data, in order to use Bayesian methods, such as BIC and Bayesian model, to build wage forecasting model in practice. […]