|
4388
|
Probabilistic Reasoning in Intelligent Systems
– Pearl
- 1988
|
|
904
|
Local computations with probabilities on graphical structures and their applications to expert systems
– Lauritzen, Spiegelhalter
- 1988
|
|
533
|
The Foundations of Statistics
– Savage, J
- 1954
|
|
411
|
The computational complexity of probabilistic inference using Bayesian belief networks
– Cooper
- 1990
|
|
361
|
Markov Decision Processes
– Puterman
- 1994
|
|
312
|
A model for reasoning about persistence and causation
– Dean, Kanazawa
- 1990
|
|
291
|
Planning and Control
– Dean, Wellman
- 1991
|
|
275
|
Evaluating Influence Diagrams
– Shachter
- 1986
|
|
175
|
Approximating probabilistic inference in Bayesian belief networks is NPhard
– Dagum, Luby
- 1993
|
|
150
|
Nonserial Dynamic Programming
– Bertele, Brioschi
- 1972
|
|
101
|
Approximations in Bayesian belief universes for knowledge based systems
– Jensen, Andersen
- 1990
|
|
100
|
Triangulated graphs and the elimination process
– Rose
- 1970
|
|
91
|
Valuation-based systems for Bayesian decision analysis
– Shenoy
- 1992
|
|
86
|
Probabilistic inference and influence diagrams
– Shachter
- 1988
|
|
84
|
Decision Analysis
– Raiffa
- 1968
|
|
79
|
Formulation of Tradeoffs in Planning Under Uncertainty
– Wellman
- 1990
|
|
77
|
A maximum entropy approach to nonmonotonic reasoning
– Goldszmidt, Morris, et al.
- 1993
|
|
75
|
Some Practical Issues in Constructing Belief Networks
– Henrion
- 1989
|
|
65
|
Dynamic programming and influence diagrams
– Tatman, Shachter
- 1990
|
|
64
|
Construction of belief and decision networks
– Breese
- 1992
|
|
55
|
A computational model for causal and diagnostic reasoning in inference engines
– Kim
- 1983
|
|
48
|
Decision making using probabilistic inference methods
– Shachter, Peot
- 1992
|
|
44
|
Approximation through multicommodity flow
– Agrawal, Klein, et al.
- 1990
|
|
42
|
A language for construction of belief networks
– Goldman, Charniak
- 1993
|
|
41
|
Probabilistic Expert Systems
– Shafer
- 1996
|
|
39
|
Structuring conditional relationships in influence diagrams
– Smith, Holtzman, et al.
- 1993
|
|
36
|
A method for using belief networks as influence diagrams
– Cooper
- 1988
|
|
36
|
From knowledge bases to decision models
– Wellman, Breese, et al.
- 1992
|
|
30
|
d�separation� From theorems to algorithms
– Verma�, Pearl
- 1989
|
|
28
|
Model-Based influence diagrams for machine vision
– Levitt, Agosta, et al.
- 1990
|
|
27
|
Coping with uncertainty in map learning
– Basye, Dean, et al.
- 1989
|
|
23
|
Influence Diagrams”, The Principles and Applications of Decision Analysis: Vol
– Howard, Matheson
- 1984
|
|
22
|
Valuation network representation and solution of asymmetric decision problems
– Shenoy
|
|
21
|
A logic and time nets for probabilistic inference
– Kanazawa
- 1991
|
|
21
|
The equivalence of exact methods for probabilistic inference on belief networks
– Shachter, Andersen, et al.
- 1991
|
|
20
|
Dynamic network construction and updating techniques for the diagnosis of acute abdominal pain
– Provan, Clarke
|
|
20
|
An Ordered Examination of Influence Diagrams
– Shachter
- 1990
|
|
20
|
The Utility of Consistency-Based Diagnostic Techniques
– Provan, Poole
- 1991
|
|
20
|
IDEAL: A software package for analysis of influence diagrams
– Srinivas, Breese
- 1990
|
|
19
|
Influence diagrams for statistical modelling
– Smith
- 1989
|
|
19
|
A causal calculus
– Good
- 1961
|
|
17
|
Decision making: Descriptive, normative and prescriptive interactions
– Bell, Raiffa, et al.
- 1988
|
|
17
|
From influence to relevance to knowledge
– Howard
- 1990
|
|
15
|
Using Influence Diagrams to Value Information and Control”, chapter
– Matheson
- 1990
|
|
13
|
Decision Analysis: a Bayesian Approach
– Smith
- 1988
|
|
12
|
Utility-based control for computer vision
– Levitt, Binford, et al.
- 1990
|
|
11
|
A dynamic approach to probabilistic inference using Bayesian networks
– Horsch, Poole
- 1990
|
|
9
|
A tutorial introduction to decision theory
– North
- 1968
|
|
9
|
Local computation in hypertrees, Working Paper No
– Shafer, Shenoy
- 1988
|
|
8
|
Refinement and Coarsening of Bayesian networks
– Chang, Fung
- 1990
|
|
