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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
5403
Venue
SIGMOD
Year
2017
Pagerank
4.5390028e-05
Overall Rank
8,334 | 42.08%
DOI
10.1145/3035918.3064026

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Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
6,914 CYADB: A Database that Covers Your Ask 2018 VLDB 4.8878659e-05
10,979 StarfishDB: a Query Execution Engine for Relational Probabilistic Programming 2024 SIGMOD 4.1905499e-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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