bayesi: A Bayesian Statistics Tutorial for Quantitative Analysts and Machine Learning Graduate Students

bayesi is a GitHub open-source tutorial project maintained by ANDERSON482, focusing on Bayesian statistics and econometrics. It aims to provide systematic learning materials for quantitative analysts and graduate students in the machine learning field, lowering the learning barrier for Bayesian methods.

Bayesian methods have gained much attention in the data science field due to their unique probabilistic interpretation framework and flexible modeling capabilities, but the learning curve for quantitative analysts and ML graduate students is relatively steep. The bayesi project was created as an open-source tutorial project to lower this barrier.

The fundamental difference between the Bayesian school and the frequentist school lies in treating probability as a quantification of the degree of belief, which brings three major advantages: 1. Directly making probabilistic statements about parameters; 2. Naturally supporting the integration of prior knowledge (especially valuable when data is scarce); 3. Providing a unified framework for handling complex models (hierarchical, mixed, non-parametric, etc.).

The tutorial covers multi-level content: Basic theory (Bayes' theorem, prior/likelihood/posterior distributions); Computational methods (MCMC, variational inference, HMC, etc.); Econometric applications (regression, time series, panel data, instrumental variables, etc.); Integration with machine learning (Bayesian optimization, neural networks, Gaussian processes, probabilistic graphical models, etc.).

The project provides code examples for mainstream tools in the Python ecosystem, including PyMC, Stan, TensorFlow Probability, NumPyro, etc., to help learners apply Bayesian methods to practical data analysis projects.

1. Quantitative analysts: Learn financial risk modeling, Bayesian time series modeling, model selection and averaging, Bayesian optimization for parameter tuning; 2. ML graduate students: Understand probabilistic graphical models, Bayesian neural networks, Gaussian processes, laying the foundation for Bayesian deep learning research.

As an open-source project, bayesi welcomes community participation: Submit content improvement suggestions, share practical application cases, supplement code examples, and carry out translation and localization work.

bayesi provides valuable open-source resources for Bayesian statistics learning, helping learners bridge the gap between theory and practice and master core skills. For quantitative analysts and researchers who wish to deeply apply Bayesian methods, it is a high-quality resource worth investing time in learning.