Topic
Variable elimination
About: Variable elimination is a research topic. Over the lifetime, 827 publications have been published within this topic receiving 34560 citations.
Papers published on a yearly basis
Papers
TL;DR: This paper presents a Bayesian method for constructing probabilistic networks from databases, focusing on constructing Bayesian belief networks, and extends the basic method to handle missing data and hidden variables.
Abstract: This paper presents a Bayesian method for constructing probabilistic networks from databases. In particular, we focus on constructing Bayesian belief networks. Potential applications include computer-assisted hypothesis testing, automated scientific discovery, and automated construction of probabilistic expert systems. We extend the basic method to handle missing data and hidden (latent) variables. We show how to perform probabilistic inference by averaging over the inferences of multiple belief networks. Results are presented of a preliminary evaluation of an algorithm for constructing a belief network from a database of cases. Finally, we relate the methods in this paper to previous work, and we discuss open problems.
4,288 citations
3,339 citations
TL;DR: In this article, it was shown that probabilistic inference using belief networks is NP-hard and that it seems unlikely that an exact algorithm can be developed to perform inference efficiently over all classes of belief networks and that research should be directed toward the design of efficient special-case, average-case and approximation algorithms.
2,200 citations
TL;DR: This review focuses on the variable selection methods in NIR spectroscopy with some classical approaches and sophisticated methods such as successive projections algorithm (SPA), uninformative variable elimination (UVE) and elaborate search-based strategies.
1,049 citations
4 Jul 2004
TL;DR: In this paper, a generalization of linear-chain CRFs, called dynamic conditional random fields (DCRFs), is proposed, in which each time slice contains a set of state variables and edges and parameters are tied across slices.
Abstract: In sequence modeling, we often wish to represent complex interaction between labels, such as when performing multiple, cascaded labeling tasks on the same sequence, or when long-range dependencies exist. We present dynamic conditional random fields (DCRFs), a generalization of linear-chain conditional random fields (CRFs) in which each time slice contains a set of state variables and edges---a distributed state representation as in dynamic Bayesian networks (DBNs)---and parameters are tied across slices. Since exact inference can be intractable in such models, we perform approximate inference using several schedules for belief propagation, including tree-based reparameterization (TRP). On a natural-language chunking task, we show that a DCRF performs better than a series of linear-chain CRFs, achieving comparable performance using only half the training data.
960 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2025 | 1 |
| 2024 | 1 |
| 2023 | 14 |
| 2022 | 24 |
| 2021 | 16 |
| 2020 | 20 |