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.
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7.4K
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260
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119
Institutions
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2017–2026
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Inference Networks for Document Retrieval
Howard R. Turtle, W. Bruce Croft · ACM SIGIR Forum · 2017 · 425 citations · Full text
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University of Massachusetts Amherst
Amherst Center, United States
9 h-index
15 publications
3.3K citations

University of Central Florida
Orlando, United States
3 h-index
6 publications
130 citations
University of California, Los Angeles
Los Angeles, United States
3 h-index
5 publications
469 citations
University of Washington
Seattle, United States
3 h-index
15 publications
802 citations
Aalborg University
Aalborg, Denmark
2 h-index
3 publications
434 citations
Tsinghua University
Beijing, China
2 h-index
5 publications
269 citations

University of Rochester
Rochester, United States
2 h-index
2 publications
227 citations
ETH Zurich
Zurich, Switzerland
2 h-index
2 publications
30 citations
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Conference · 3 publications · 80 citations
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TPAMI · 3 publications · 452 citations
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Repository · 3 publications · 276 citations
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2 publications · 450 citations
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