Bayesian Inference in Census-House Dataset

As one of the most popular probabilistic programming tools, PyMC3 can solve inference problems in many scientific fields. In this paper, we used PyMC3 to build a Bayesian model for the census-house dataset to predict the correspondence between the U.S. population and house prices, and evaluated it using the dataset to determine the validity and accuracy of the established model. Through the evaluation of this dataset, the Bayesian model established in this paper can predict the theoretical data of house prices with high accuracy in the absence of COVID-19, which has implications for the study of the current property prices that have increased significantly because of COVID-19 and the due prices of similar large assets, researchers can predict the house prices in the absence of COVID-19, and then based on the current house prices calculate the difference and thus study the impact of COVID-19 in terms of house prices as well as the impact of similar asset prices.