> For the complete documentation index, see [llms.txt](https://bayesians.gitbook.io/bayesian/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://bayesians.gitbook.io/bayesian/background-and-origin/1.3-bayes-theorem.md).

# 1.3 Bayes' theorem

Bayes is the originator of the famous statistics. Bayesian statistics is a theory in the field of statistics that is based on the Bayesian interpretation of probability, where probability represents the degree of belief in an event. The degree of belief can be based on prior knowledge about the event, such as the results of previous experiments, or on personal beliefs about the event. 

Bayesian network, also known as belief network or directed acyclic graph model, is a probabilistic graph model, which can know the properties of a group of random variables and their n groups of conditional probability distribution by means of a directed acyclic graph. The development of Bayesian Decentralized Computing Network Protocol (BDCP) was inspired by Bayesian networks, especially the transmission of information flow over directed acyclic graphs.