The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
A differential approach to inference in Bayesian networks
Open accessAbstract
We present a new approach to inference in Bayesian networks, which is based on representing the network using a polynomial and then retrieving answers to probabilistic queries by evaluating and differentiating the polynomial. The network polynomial itself is exponential in size, but we show how it can be computed efficiently using an arithmetic circuit that can be evaluated and differentiated in time and space linear in the circuit size. The proposed framework for inference subsumes one of the most influential methods for inference in Bayesian networks, known as the tree-clustering or jointree method, which provides a deeper understanding of this classical method and lifts its desirable characteristics to a much more general setting. We discuss some theoretical and practical implications of this subsumption.
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Cites
Graph-Based Algorithms for Boolean Function Manipulation
Bryant (1986) · IEEE Transactions on Computers · doi:10.1109/tc.1986.1676819 · Cited by 8,891
Introduction to Bayesian Networks
(2019) · Operational Risk Modelling in Financial Services · doi:10.1002/9781119508557.ch12 · Cited by 2,569
Probabilistic Networks and Expert Systems
(1999) · Information Science and Statistics · doi:10.1007/b97670 · Cited by 1,727
A Linear-Time Algorithm for Finding Tree-Decompositions of Small Treewidth
Bodlaender, H. L. (1996) · SIAM Journal on Computing · doi:10.1137/s0097539793251219 · Cited by 1,542
(1988) · doi:10.1016/b978-0-08-051489-5.50008-4 · Cited by 1,429
Darwiche, A. Y., & Marquis, P. (2002) · Journal of Artificial Intelligence Research · doi:10.1613/jair.989 · Cited by 903
Self-citationThe EM algorithm for graphical association models with missing data
Lauritzen, S. L. (1995) · Computational Statistics & Data Analysis · doi:10.1016/0167-9473(93)e0056-a · Cited by 760
Bucket Elimination: A Unifying Framework for Probabilistic Inference
Dechter, R. (1998) · Learning in Graphical Models · doi:10.1007/978-94-011-5014-9_4 · Cited by 452
Inference in belief networks: A procedural guide
Huang, C., & Darwiche, A. (1996) · International Journal of Approximate Reasoning · doi:10.1016/s0888-613x(96)00069-2 · Cited by 450
Self-citationExploiting Causal Independence in Bayesian Network Inference
Zhang, N. L., & Poole, D. (1996) · Journal of Artificial Intelligence Research · doi:10.1613/jair.305 · Cited by 433
Cited by
Bayesian networks for interpretable machine learning and optimization
Mihaljević, B., Bielza, C., & Larrañaga, P. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.01.138 · Cited by 123
Safe Reinforcement Learning via Probabilistic Logic Shields
Yang, W.-C., Marra, G., Rens, G., & De Raedt, L. (2023) · Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2023/637 · Cited by 34
Metadata
- Authors
- Darwiche, A.
- Venue
- Journal of the ACM
- Published
- 2003-05
- Volume
- 50
- Issue
- 3
- Pages
- 280-305
- ISSN
- 0004-5411 (ISSN-L)
- 1557-735X
- OpenAlex
- W2139546855
- Cited by
- 470
- References
- 14
- FWCI
- 10.25
- Type
- journal article
- Language
- en
- Open access
- green
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