Qualitative probabilistic networks have been introduced as qualitative abstractions of Bayesian belief networks. One of the major drawbacks of these qualitative networks is their coarse level of detail, which may lead to unresolved trade-offs during inference. We present an enhanced formalism for qualitative networks with a finer level of detail. An enhanced qualitative probabilistic network differs from a regular qualitative network in that it distinguishes between strong and weak influences. Enhanced qualitative probabilistic networks are purely qualitative in nature, as regular qualitative networks are, yet allow for efficiently resolving trade-offs during inference.
机构:
Hop Cochin, AP HP, Dept Radiol, F-75014 Paris, France
Univ Paris Cite, Fac Med, F-75006 Paris, FranceHop Cochin, AP HP, Dept Radiol, F-75014 Paris, France
机构:
Nanjing Univ, State Key Lab Novel Software Technol, Nanjing, Peoples R ChinaNanjing Univ, State Key Lab Novel Software Technol, Nanjing, Peoples R China
Chen, Haimin
Jiang, Yonggang
论文数: 0引用数: 0
h-index: 0
机构:
Nanjing Univ, State Key Lab Novel Software Technol, Nanjing, Peoples R ChinaNanjing Univ, State Key Lab Novel Software Technol, Nanjing, Peoples R China
Jiang, Yonggang
Zheng, Chaodong
论文数: 0引用数: 0
h-index: 0
机构:
Nanjing Univ, State Key Lab Novel Software Technol, Nanjing, Peoples R ChinaNanjing Univ, State Key Lab Novel Software Technol, Nanjing, Peoples R China
Zheng, Chaodong
PROCEEDINGS OF THE 2021 ACM SYMPOSIUM ON PRINCIPLES OF DISTRIBUTED COMPUTING (PODC '21),
2021,
: 139
-
149