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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Abstract

Bayesian Abstract

This paper introduces the Bayesian Decentralized Computing Network Protocol (BDCP), a protocol designed to build decentralized computing networks. This network is a peer-to-peer network formed by the availability of cloud computing resources on decentralized computer nodes, and no entity has central control over the network.

WEB3.0 is based on decentralized proprietary services, they are Internet-wide open services where participants form a decentralized network providing useful services without a central management or trusting party. BDCP provides computing infrastructure for WEB3.0 services.

BDCP departs from the current distributed cluster model, which delegates access control of central nodes and data to a centralized trusted authority. Instead, BDCP empowers peer-to-peer models, providing decentralized computing networks, computing-go-to-data models, distributed machine learning, and new mechanisms to protect user data ownership.

Note: Active research work is underway on BCDP and its network, and a new version of this paper will be published at https://www.bayesian.global. For comments and suggestions, please contact us at research@bayesian.global.

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Last updated 2 years ago

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