Dan Geiger

Probabilistic & Bayesian Methods · 1990–2006

A researcher in the Science corpus: 8 papers; trusts 12 researchers and is trusted by 129 researchers here, as inferred from the citation graph.

Subfields

• Probabilistic & Bayesian Methods — 24 citations into it; #135 of 1,525 by trust here, #179 by citations
• Statistics and Probability — 8 citations into it
• Artificial Intelligence — 5 citations into it; #19,654 of 35,345 by trust here, #28,481 by citations

Whom Dan Geiger trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Judea Pearl
2. David E. Heckerman
3. Alexander Schrijver
4. Lawrence D. Brown
5. Ron Kohavi
6. Donald B. Rubin
7. Peter Spirtes
8. Wray L. Buntine
9. B. D. Ripley
10. David J. Spiegelhalter

Who trusts Dan Geiger overall

1. Jack Kuipers
2. Daniel Eaton
3. Antti Hyttinen
4. Bernhard Schölkopf
5. David E. Heckerman
6. Giusi Moffa
7. Kevin P. Murphy
8. Rónán Daly
9. Florian Markowetz
10. Lars Lorch

Papers from Dan Geiger's perspective

Ordered by how strongly the researchers Dan Geiger trusts endorse each paper. Same corpus, a different order for every researcher.

1. Probabilistic reasoning in intelligent systems: Networks of plausible inference
2. Probabilistic reasoning in intelligent systems - networks of plausible inference
3. Information Theory: 1948-1998 - Guest Editorial
4. Approximating discrete probability distributions with dependence trees
5. An Introduction to Variational Methods for Graphical Models
6. Probabilistic reasoning in intelligent systems: Networks of plausible inference
7. Causation, prediction, and search
8. A View of the Em Algorithm that Justifies Incremental, Sparse, and other Variants
9. INFERENCE AND MISSING DATA
10. Gradient-based learning applied to document recognition

Their papers

• On the toric algebra of graphical models (2006) — 209 citations
• Efficient approximations for learning phylogenetic HMM models from data (2004) — 44 citations
• Parameter Priors for Directed Acyclic Graphical Models and the Characteriration of Several Probability Distributions (1999)
• Bayesian Network Classifiers (1997)
• Learning Bayesian Networks: Search Methods and Experimental Results (1995)
• Learning Bayesian networks: The combination of knowledge and statistical data (1994)
• Learning Gaussian Networks (1994)
• Logical and algorithmic properties of independence and their application to Bayesian networks (1990)