Abstract: In Bayesian model, it is often impossible to calculate the posterior analytically, and in practice, it relies on approximate inference. Variational Inference (VI) is an important deterministic approximate inference method, which has a higher computational efficiency than Markov Chain Monte Carlo (MCMC) sampling, and has a distinct advantage in the era of big data. This paper reviewed classical VI, analyzed Stochastic Variational Inference (SVI) and its application in subject model, reviewed more robust VI, summarized the research progress of probabilistic programming system, and prospected the future development trend of VI.