UAI 1996 - PDF Preprint Papers

UAI 1996

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UAI
1996

168views Artificial Intelligence» more UAI 1996»

Bayesian Learning of Loglinear Models for Neural Connectivity

13 years 5 months ago

Download www.mpib-berlin.mpg.de

This paper presents a Bayesian approach to learning the connectivity structure of a group of neurons from data on configuration frequencies. A major objective of the research is t...

Kathryn B. Laskey, Laura Martignon

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Id	Views	Title	Status

1	168	Bayesian Learning of Loglinear Models for Neural Connectivity	claim
2	151	Learning Equivalence Classes of Bayesian Network Structures	claim
3	147	An evaluation of structural parameters for probabilistic reasoning: Results on benchmark circuits	claim
4	146	Context-Specific Independence in Bayesian Networks	claim
5	139	Binary Join Trees	claim
6	135	Asymptotic Model Selection for Directed Networks with Hidden Variables	claim
7	132	Critical Remarks on Single Link Search in Learning Belief Networks	claim
8	126	Some Experiments with Real-time Decision Algorithms	claim
9	126	Computing upper and lower bounds on likelihoods in intractable networks	claim
10	120	Efficient Approximations for the Marginal Likelihood of Incomplete Data Given a Bayesian Network	claim
11	120	Bucket elimination: A unifying framework for probabilistic inference	claim
12	111	Network Engineering for Complex Belief Networks	claim
13	111	A Discovery Algorithm for Directed Cyclic Graphs	claim
14	109	A Graph-Theoretic Analysis of Information Value	claim
15	108	Learning Bayesian Networks with Local Structure	claim
16	107	Efficient Search-Based Inference for noisy-OR Belief Networks: TopEpsilon	claim
17	105	A Qualitative Markov Assumption and Its Implications for Belief Change	claim
18	102	Why is diagnosis using belief networks insensitive to imprecision in probabilities?	claim
19	101	Decision-Theoretic Troubleshooting: A Framework for Repair and Experiment	claim
20	94	Topological parameters for time-space tradeoff	claim
21	84	Query DAGs: A practical paradigm for implementing belief-network inference	claim
22	84	Sound Abstraction of Probabilistic Actions in The Constraint Mass Assignment Framework	claim
23	70	A Measure of Decision Flexibility	claim

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