• ## 40 questions for testing time series

Time：2021-10-10

Author | Saurabh jajuCompile FlinSource | analyticsvidhya introduce Time series prediction and modeling play an important role in data analysis. Time series analysis is a branch of statistics, which is widely used in econometrics, operations research and other fields. This skill test article is to test your understanding of the concept of time series. A […]

• ## 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 […]

• ## The practical application of R language Fama French three factor model: portfolio optimization

Time：2021-4-8

Link to the original text:http://tecdat.cn/?p=20360  This paper will explain the R language in financial mathematics to optimize the portfolio, the implementation and use of factor model. Macroeconomic factor model with single market factor We’ll start with a simple example of a single known factor, the market index. The model is ​ The explicit factor ft […]

• ## Linear discriminant analysis, LDA

Time：2020-12-20

Linear discriminant classifier consists of vector \$W \$and deviation term \$B \$. Given the example \$x \$, it predicts the category tag \$y \$according to the following rules, that is\$y=sign(w^Tx+b)\$The column vector is represented by lowercase and row vector is represented by transposition.The classification process is divided into two steps Firstly, the weight vector w […]

• ## Automatic driving system positioning and state estimation – Recursive Least Squares Estimation

Time：2020-9-18

The above-mentioned ordinary least squares estimation and weighted least squares estimation assume that all measurement data are collected in advance. But in practical application, the measurement data may be flow type, for example, the position measurement system continuously measures the vehicle position at the frequency of 100Hz. In the high-frequency update frequency, there are more […]

• ## Automatic driving positioning system Kalman filter

Time：2020-9-15

Kalman filter is an ideal method to deal with continuously changing dynamic uncertain systems. Because of its small memory consumption (no need to record historical state) and fast running speed, it is widely used in real-time multi-sensor fusion system of robot. What can we do with a Kalman filter Let’s first look at a simple […]

• ## Automatic driving positioning system unscented Kalman filter

Time：2020-9-12

Unscented Kalman filter is another way to solve nonlinear Kalman filter. It uses unscented transform to solve the problem of nonlinear transformation of probability distribution. Unscented Kalman filter does not need to calculate Jacobin matrix like extended Kalman filter, and it can obtain more accurate nonlinear processing effect under the condition of approximately the same […]

• ## Deep Interpretation of Covariance

Time：2019-6-17

In machine learning, covariance is used, but the meaning of covariance is not very clear before. Today, we will focus on it. Basic concepts of Statistics Children who have studied probability statistics know that the most basic concepts in statistics are the mean, variance, or standard deviation of samples. First of all, we give you […]