Bayesian
  • 💚Bayesian
  • 🟢Abstract
  • 🟢Background & Origin
    • 🟩1.0 Innovation in AI and Blockchain
    • 🟩1.2 Supercomputing is the future
    • 🟩1.3 Bayes' theorem
    • 🟩1.4 Bayesian Global AI
    • 🟩1.5 BDCP
  • 🟢Architecture
    • 🟩2.1 Technical protocol
    • 🟩2.2 Network layer
    • 🟩2.3 Data Layer
    • 🟩2.4 Model layers
    • 🟩2.5 Application Layer
    • 🟩2.6 Value Layer
  • 🟢Platform overview
    • 🟩3.1 Token distribution
    • 🟩3.2 Deflationary value
  • 🟢Ecosystem
    • 🟩4.1 Highlights & Advantages
    • 🟩4.2 Activation plan
    • 🟩4.3 Bayesian Network Protocol
    • 🟩4.4 Supported Items
    • 🟩4.5 WEB3 and the Metaverse
  • 🟢Bayesian Foundation
    • 🟩Bayesian Foundation Ld.
    • 🟩5.1 Strategic Decision Committee
    • 🟩5.2 Technology Development Committee
    • 🟩5.3 Public Relations Committee
    • 🟩5.4 Secretariat
    • 🟩5.5 Scientists
  • 🟢Development line
    • 🟩6.1 Development Route
    • 🟩6.2 The first cooperative institutions
  • 🟢References
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  1. Background & Origin

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.

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Last updated 2 years ago

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