Probabilistic Inference Network Model

The Probabilistic Inference Network Model is a formal framework for information retrieval that structurally integrates probabilistic reasoning within a directed graph of evidence and hypotheses, distinguishing itself by explicitly modeling the conditional dependencies between queries, documents, and relevance through Bayesian inference.

84

Publications

7.4K

Citations

260

Authors

119

Institutions

Publications per year

2017–2026

22

Authors

260

  1. WB
    W. Bruce Croft

    University of Massachusetts Amherst, United States

    10 publications · 2K citations

    10

    h-index

  2. HR
    Howard R. Turtle

    University of Massachusetts Amherst, United States

    6 publications · 1.5K citations

    6

    h-index

  3. TH
    Tao Hai

    Qiannan Normal College For Nationalities, China

    1 publications · 16 citations

    1

    h-index

  4. MS
    Michael Skinner

    Defence Science and Technology Group, Australia

    1 publications · 37 citations

    1

    h-index

  5. MZ
    Ming Zhou

    Microsoft Research Asia (China), China

    1 publications · 24 citations

    1

    h-index

1–5 of 260

Venues

  1. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 3 publications · 80 citations

    3

    h-index

  2. 3

    h-index

  3. arXiv (Cornell University)

    Repository · 3 publications · 276 citations

    3

    h-index

  4. The Computer Journal

    2 publications · 450 citations

    2

    h-index

  5. Information Processing & Management

    2 publications · 49 citations

    2

    h-index

1–5