Abstract
Bayesian statistics has a lot of influence on neural networks and deep learning for artificial intelligence (AI). The inference and learning of Bayesian statistics is based on prior, likelihood and posterior. The prior is the current belief of data field and the posterior is the updated belief after learning from observed data. By repeated learning using prior and posterior distributions, Bayesian statistics provides advanced data learning for AI. In this paper, we compare the previous Bayesian inference and learning methods for AI and propose a model based on Bayesian inference and learning for neural networks and deep learning.
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