Bayes-based air pollution computer measurement and big data outlier detection model

Abstract

In today’s rapidly changing social environment, high-dimensional data abound, but not all data are meaningful. In order to reduce costs and reduce consumption, people are more interested in some important variables. Therefore, variable selection of high-dimensional data is an important research direction. Based on the Bayesian method, it stands out among many variable selection methods with effective estimation efficiency and flexible mechanism, and it is also suitable for parametric and non-parametric models. Thanks to the flexibility of the statistical conclusion inference process, combined with prior information, the actual results are more accurate. At present, many indoor pollution problems, including decoration pollution, are getting more and more attention, and its air pollution calculations are obviously very important. Indoor air quality not only directly affects the comfort of the human body, but also significantly affects the work efficiency of family members. As a result of the development of the new digital economy, digital mining technology based on big data has brought unprecedented new development methods to various fields. This paper establishes an outlier detection model, identifies data processes that are significantly different from the normal data set or expected value, and cleans the data to improve the accuracy of the integrated detection model. This paper proposes a solution to this problem by combining big data mining technology with the establishment of an outlier detection model. At the same time, it optimizes the design of the model to reduce errors, so as to realize its accurate application in the measurement of air pollutants, and aims to promote its development.