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Beta Probabilistic Databases: A Scalable Approach to Belief Updating and Parameter Learning

Summary: Beta Probabilistic Databases (B-PDBs) model each tuple probability as a Beta latent variable, enabling principled belief updates from noisy, indirect evidence. Remains TI-PDB-compatible, enabling scalable Bayesian updates and soft-EM learning in-database. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
5402
Venue
SIGMOD
Year
2017
Pagerank
4.5433598e-05
Overall Rank
8,340 | 41.99%
DOI
10.1145/3035918.3064026

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Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
6,912 CYADB: A Database that Covers Your Ask 2018 VLDB 4.8925595e-05
10,976 StarfishDB: a Query Execution Engine for Relational Probabilistic Programming 2024 SIGMOD 4.1945683e-05
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

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