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## Bayesian Network Modelling through Qualitative Patterns (2005)

Citations: | 15 - 5 self |

### Citations

8894 |
Probabilistic Reasoning in Intelligent Systems
- Pearl
- 1988
(Show Context)
Citation Context ... as the certainty-factor calculus [1, 19], Dempster-Shafer theory [18], possibilistic logic [7], fuzzy logic [24], and Bayesian networks, also called belief networks and causal probabilistic networks =-=[2, 16, 15]-=-. During the last decade a gradual shift towards the use of probability theory as the foundation of almost all of the work in the area could be observed, mainly due to the impact, both theoretically a... |

3130 |
A mathematical theory of evidence
- Shafer
- 1976
(Show Context)
Citation Context ... Many di#erent methods for representing and reasoning with uncertain knowledge have been developed during the last three decades, such as the certainty-factor calculus [1, 19], Dempster-Shafer theory =-=[18]-=-, possibilistic logic [7], fuzzy logic [24], and Bayesian networks, also called belief networks and causal probabilistic networks [2, 16, 15]. During the last decade a gradual shift towards the use of... |

1520 |
Local computations with probabilities on graphical structures and their application to expert systems
- Lauritzen, Spiegelhalter
- 1988
(Show Context)
Citation Context ...nt probability distribution on its variables and thus provides for computing any probability of interest. Various di#erent algorithms for probabilistic inference with a Bayesian network are available =-=[16, 25, 20]-=-. A real-life example is shown in Figure 1. In this network, it is modelled that patients may become colonised by specific bacteria, for example P. aeruginosa, after admission to a hospital. As the ac... |

858 |
Bayesian Networks and Decision Graphs
- Jensen, Nielsen
- 2009
(Show Context)
Citation Context ... as the certainty-factor calculus [1, 19], Dempster-Shafer theory [18], possibilistic logic [7], fuzzy logic [24], and Bayesian networks, also called belief networks and causal probabilistic networks =-=[2, 16, 15]-=-. During the last decade a gradual shift towards the use of probability theory as the foundation of almost all of the work in the area could be observed, mainly due to the impact, both theoretically a... |

771 |
Probabilistic Networks and Expert Systems
- Cowell, Dawid, et al.
- 1999
(Show Context)
Citation Context ... as the certainty-factor calculus [1, 19], Dempster-Shafer theory [18], possibilistic logic [7], fuzzy logic [24], and Bayesian networks, also called belief networks and causal probabilistic networks =-=[2, 16, 15]-=-. During the last decade a gradual shift towards the use of probability theory as the foundation of almost all of the work in the area could be observed, mainly due to the impact, both theoretically a... |

743 |
A Mathematical Introduction to Logic
- Enderton
- 2001
(Show Context)
Citation Context ...ts a logical OR and a MAX function, respectively. 5 2.2.2 Boolean functions The function f in equation (2) is actually a Boolean function; recall that there are 2 2 n di#erent n-ary Boolean functions =-=[8, 22]-=-. Hence, the potential number of causal interaction models is huge. The Boolean functions can also be represented by the probabilities Pr(e | I 1 , . . . , I n ) in equation (3), with Pr(e | I 1 , . .... |

409 | The Complexity of Boolean Functions
- Wegener
- 1987
(Show Context)
Citation Context ...ts a logical OR and a MAX function, respectively. 5 2.2.2 Boolean functions The function f in equation (2) is actually a Boolean function; recall that there are 2 2 n di#erent n-ary Boolean functions =-=[8, 22]-=-. Hence, the potential number of causal interaction models is huge. The Boolean functions can also be represented by the probabilities Pr(e | I 1 , . . . , I n ) in equation (3), with Pr(e | I 1 , . .... |

384 |
Rule-Based Expert Systems: The MYCIN Experiments of the Stanford Heuristic Programming Project,
- Buchanan, Shortliffe
- 1984
(Show Context)
Citation Context ... at least since the early 1970s. Many di#erent methods for representing and reasoning with uncertain knowledge have been developed during the last three decades, such as the certainty-factor calculus =-=[1, 19]-=-, Dempster-Shafer theory [18], possibilistic logic [7], fuzzy logic [24], and Bayesian networks, also called belief networks and causal probabilistic networks [2, 16, 15]. During the last decade a gra... |

275 | Possibilistic logic
- Dubois, Lang, et al.
- 1994
(Show Context)
Citation Context ... representing and reasoning with uncertain knowledge have been developed during the last three decades, such as the certainty-factor calculus [1, 19], Dempster-Shafer theory [18], possibilistic logic =-=[7]-=-, fuzzy logic [24], and Bayesian networks, also called belief networks and causal probabilistic networks [2, 16, 15]. During the last decade a gradual shift towards the use of probability theory as th... |

181 | Exploiting Causal Independence in Bayesian Network Inference”,
- Zhang, Poole
- 1996
(Show Context)
Citation Context ...ntly used in practical networks for situations where probability distributions are complex. The theory has also been exploited to increase the e#ciency of probabilistic inference in Bayesian networks =-=[25, 26]-=-. A limitation of the theory of causal independence is that it is usually unclear with what sort of qualitative behaviour a network will be endowed when choosing for a particular interaction type. As ... |

153 | Fundamental concepts of qualitative probabilistic networks.
- Wellman
- 1990
(Show Context)
Citation Context ...he formalism of Bayesian networks. They allow describing the dynamics of interaction among variables in a purely qualitative fashion by means of the specification and propagation of qualitative signs =-=[17, 23]-=-. Hence, qualitative probabilistic networks abstract from the numerical detail. The aim of the present work was to develop a theory of qualitative, causal interaction patterns,sQC patterns for short, ... |

138 |
A model of inexact reasoning in medicine.
- Shortliffe, Buchanan
- 1975
(Show Context)
Citation Context ... at least since the early 1970s. Many di#erent methods for representing and reasoning with uncertain knowledge have been developed during the last three decades, such as the certainty-factor calculus =-=[1, 19]-=-, Dempster-Shafer theory [18], possibilistic logic [7], fuzzy logic [24], and Bayesian networks, also called belief networks and causal probabilistic networks [2, 16, 15]. During the last decade a gra... |

123 |
Some practical issues in constructing belief networks.
- Henrion
- 1989
(Show Context)
Citation Context ...ar interaction type. As a consequence, only two types of interaction are in frequent use: the noisy-OR and the noisy-MAX; in both cases, interactions among variables are modelled as being disjunctive =-=[3, 13, 16]-=-. Qualitative probabilistic networks o#er a qualitative analogue to the formalism of Bayesian networks. They allow describing the dynamics of interaction among variables in a purely qualitative fashio... |

117 |
thinking: The foundations of probability and its applications
- Good
- 1983
(Show Context)
Citation Context ...ng independence assumptions. 2.2.1 Probabilistic representation One popular way to specify interactions among statistical variables in a compact fashion is o#ered by the notion of causal independence =-=[9, 10, 11, 12]-=-. The global structure of a causalindependence model is shown in Figure 2; it expresses the idea that causes C 1 , . . . , C n influence a given common e#ect E through intermediate variables I 1 , . .... |

105 | MUNIN—A causal probabilistic network for interpretation of electromyographic findings,” - Andreassen, Wolbye, et al. - 1987 |

99 |
Fuzzy logic and approximate reasoning
- Zadeh
- 1975
(Show Context)
Citation Context ... reasoning with uncertain knowledge have been developed during the last three decades, such as the certainty-factor calculus [1, 19], Dempster-Shafer theory [18], possibilistic logic [7], fuzzy logic =-=[24]-=-, and Bayesian networks, also called belief networks and causal probabilistic networks [2, 16, 15]. During the last decade a gradual shift towards the use of probability theory as the foundation of al... |

78 |
A generalization of noisy-or model.
- Srinivas
- 1993
(Show Context)
Citation Context ... g k (I k , I k+1 ) are identical for each k; a function g k (I k , I k+1 ) may therefore be simply denoted by g(I, I # ). Typical examples of decomposable causal independence models are the noisy-OR =-=[3, 9, 13, 16, 21]-=- and noisy-MAX [3, 12, 21] models, where the function g represents a logical OR and a MAX function, respectively. 5 2.2.2 Boolean functions The function f in equation (2) is actually a Boolean functio... |

76 | A new look at causal independence.
- Heckerman, Breese
- 1994
(Show Context)
Citation Context ...ng independence assumptions. 2.2.1 Probabilistic representation One popular way to specify interactions among statistical variables in a compact fashion is o#ered by the notion of causal independence =-=[9, 10, 11, 12]-=-. The global structure of a causalindependence model is shown in Figure 2; it expresses the idea that causes C 1 , . . . , C n influence a given common e#ect E through intermediate variables I 1 , . .... |

74 | Parameter adjustment in Bayes networks. The generalized noisy OR-gate - Diez - 1993 |

68 | Efficient reasoning in qualitative probabilistic networks. - Druzdzel, Henrion - 1993 |

60 | der Gaag. Elicitation of probabilities for belief networks: combining qualitative and quantitative information - Druzdzel, van - 1995 |

60 | Fuzzy logic and approximate reasoning, Synthese 30 - Zadeh - 1975 |

49 | Causal independence for knowledge acquisition and inference. Also in this proceedings.
- Heckerman
- 1993
(Show Context)
Citation Context ...ng independence assumptions. 2.2.1 Probabilistic representation One popular way to specify interactions among statistical variables in a compact fashion is o#ered by the notion of causal independence =-=[9, 10, 11, 12]-=-. The global structure of a causalindependence model is shown in Figure 2; it expresses the idea that causes C 1 , . . . , C n influence a given common e#ect E through intermediate variables I 1 , . .... |

49 |
Qualitative propagation and scenario-based approaches to explanation of probabilistic reasoning
- Henrion, Druzdzel
- 1990
(Show Context)
Citation Context ...h. A product synergy between three variables expresses how the value of one variable influences the probabilities of the values of another variable in view of an observed value for the third variable =-=[14]-=-. For example, a negative product synergy of a variable A on a variable B given the value # for their common e#ect C, denoted X - ({A, B}, c), expresses that, given c, the value # for A renders the va... |

35 | Toward normative expert systems: Part II — Probability-based representations for efficient knowledge acquisition and inference - Heckerman, Nathwani - 1992 |

30 | eds., Rule-Based Expert Systems: The - Buchanan, Shortliffe - 1984 |

28 | Computer-based decision-support in the management of primary gastric non-Hodgkin lymphoma. - Lucas, Boot, et al. - 1998 |

26 | A probabilistic and decision-theoretic approach to the management of infectious disease at the ICU. - Lucas, Bruijn, et al. - 2000 |

25 |
Causal independence for probabilistic assessment and inference using Bayesian networks
- Heckerman, JS
- 1996
(Show Context)
Citation Context .... Researchers have therefore identified special types of independence relationships in order to facilitate probability assessment. In particular the theory of causal independence fulfils this purpose =-=[12]-=-. The theory allows for the specification of the interactions among variables in terms of cause-e#ect relationships and functions, adopting particular statistical independence assumptions. Causal inde... |

14 | Diaval: a bayesian expert system for echocardiography - ez, Mira, et al. - 1997 |

14 | Qualitative Approaches to Quantifying Probabilistic Network. - Renooij - 2001 |

14 | DIAVAL, a Bayesian expert system for echocardiography". - Díez, Mira, et al. - 1997 |

8 | Independence of Causal Influence and Clique Tree Propagation.
- Zhang, Yan
- 1997
(Show Context)
Citation Context ...ntly used in practical networks for situations where probability distributions are complex. The theory has also been exploited to increase the e#ciency of probabilistic inference in Bayesian networks =-=[25, 26]-=-. A limitation of the theory of causal independence is that it is usually unclear with what sort of qualitative behaviour a network will be endowed when choosing for a particular interaction type. As ... |

7 |
Parameter adjustment in bayes networks. the generalized noisy or-gate
- Dez
- 1993
(Show Context)
Citation Context ...ar interaction type. As a consequence, only two types of interaction are in frequent use: the noisy-OR and the noisy-MAX; in both cases, interactions among variables are modelled as being disjunctive =-=[3, 13, 16]-=-. Qualitative probabilistic networks o#er a qualitative analogue to the formalism of Bayesian networks. They allow describing the dynamics of interaction among variables in a purely qualitative fashio... |

2 | Toward normative expert systems: Part I-The Pathfinder project - Heckerman, Horvitz, et al. - 1992 |