Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Search-based methods to bound diagnostic probabilities in very large belief nets
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Valuation-based systems for Bayesian decision analysis
Operations Research
Reformulating inference problems through selective conditioning
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Approximating probabilistic inference in Bayesian belief networks is NP-hard
Artificial Intelligence
Computers and Biomedical Research
Blocking Gibbs sampling in very large probabilistic expert systems
International Journal of Human-Computer Studies - Special issue: real-world applications of uncertain reasoning
Bucket elimination: a unifying framework for probabilistic inference
Learning in graphical models
Introduction to Monte Carlo methods
Learning in graphical models
An Introduction to Variational Methods for Graphical Models
Machine Learning
Introduction to Bayesian Networks
Introduction to Bayesian Networks
Simulation Approaches to General Probabilistic Inference on Belief Networks
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
A Tractable Inference Algorithm for Diagnosing Multiple Diseases
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
Weighing and Integrating Evidence for Stochastic Simulation in Bayesian Networks
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
Variational methods for inference and estimation in graphical models
Variational methods for inference and estimation in graphical models
Probabilistic partial evaluation: exploiting rule structure in probabilistic inference
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Mini-buckets: a general scheme for generating approximations in automated reasoning
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
An Introduction to Variational Methods for Graphical Models
Machine Learning
Stochastic lambda calculus and monads of probability distributions
POPL '02 Proceedings of the 29th ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Bayesian Methods for Elucidating Genetic Regulatory Networks
IEEE Intelligent Systems
Adapting Kernels by Variational Approach in SVM
AI '02 Proceedings of the 15th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Accuracy vs. efficiency trade-offs in probabilistic diagnosis
Eighteenth national conference on Artificial intelligence
Noisy-OR Component Analysis and its Application to Link Analysis
The Journal of Machine Learning Research
Loop Corrections for Approximate Inference on Factor Graphs
The Journal of Machine Learning Research
Inference in the Promedas Medical Expert System
AIME '07 Proceedings of the 11th conference on Artificial Intelligence in Medicine
Graphical Models, Exponential Families, and Variational Inference
Foundations and Trends® in Machine Learning
Journal of Artificial Intelligence Research
Convexity arguments for efficient minimization of the Bethe and Kikuchi free energies
Journal of Artificial Intelligence Research
Mean-field methods for a special class of belief networks
Journal of Artificial Intelligence Research
Efficient stochastic local search for MPE solving
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Collaborative scheduling of DAG structured computations on multicore processors
Proceedings of the 7th ACM international conference on Computing frontiers
CLP(BN): constraint logic programming for probabilistic knowledge
Probabilistic inductive logic programming
Understanding the scalability of Bayesian network inference using clique tree growth curves
Artificial Intelligence
Efficient active probing for fault diagnosis in large scale and noisy networks
INFOCOM'10 Proceedings of the 29th conference on Information communications
Norm-product belief propagation: primal-dual message-passing for approximate inference
IEEE Transactions on Information Theory
Active tuples-based scheme for bounding posterior beliefs
Journal of Artificial Intelligence Research
Parallel evidence propagation on multicore processors
The Journal of Supercomputing
BLR-D: applying bilinear logistic regression to factored diagnosis problems
SLAML '11 Managing Large-scale Systems via the Analysis of System Logs and the Application of Machine Learning Techniques
Gates for handling occlusion in Bayesian models of images: an initial study
KI'11 Proceedings of the 34th Annual German conference on Advances in artificial intelligence
A variational approximation for Bayesian networks with discrete and continuous latent variables
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Loopy belief propagation for approximate inference: an empirical study
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Variational approximations between mean field theory and the junction tree algorithm
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
Recognition networks for approximate inference in BN20 networks
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
BLR-D: applying bilinear logistic regression to factored diagnosis problems
ACM SIGOPS Operating Systems Review
Review: learning bayesian networks: Approaches and issues
The Knowledge Engineering Review
Multi-assignment clustering for boolean data
The Journal of Machine Learning Research
| Hi-index | 0.06 |
We describe a variational approximation method for efficient inference in large-scale probabilistic models. Variational methods are deterministic procedures that provide approximations to marginal and conditional probabilities of interest. They provide alternatives to approximate inference methods based on stochastic sampling or search. We describe a variational approach to the problem of diagnostic inference in the "Quick Medical Reference" (QMR) network. The QMR network is a large-scale probabilistic graphical model built on statistical and expert knowledge. Exact probabilistic inference is infeasible in this model for all but a small set of cases. We evaluate our variational inference algorithm on a large set of diagnostic test cases, comparing the algorithm to a state-of-the-art stochastic sampling method.