Towards a fuzzy Bayesian network based approach for safety risk analysis of tunnel‐induced pipeline damage
Tunneling excavation is bound to produce significant disturbances to surrounding
environments, and the tunnel‐induced damage to adjacent underground buried pipelines is …
environments, and the tunnel‐induced damage to adjacent underground buried pipelines is …
[HTML][HTML] The role of local partial independence in learning of Bayesian networks
Bayesian networks are one of the most widely used tools for modeling multivariate systems.
It has been demonstrated that more expressive models, which can capture additional …
It has been demonstrated that more expressive models, which can capture additional …
Bayesian inference for vertex-series-parallel partial orders
C Jiang, GK Nicholls, JE Lee - Uncertainty in Artificial …, 2023 - proceedings.mlr.press
Partial orders are a natural model for the social hierarchies that may constrain “queue-like”
rank-order data. However, the computational cost of counting the linear extensions of a …
rank-order data. However, the computational cost of counting the linear extensions of a …
Bayesian network-based formulation and analysis for toll road utilization supported by traffic information provision
Congestion pricing has been proposed and investigated as an effective means of optimizing
traffic assignment, alleviating congestion, and enhancing traffic operation efficiencies …
traffic assignment, alleviating congestion, and enhancing traffic operation efficiencies …
[HTML][HTML] Asymmetric hidden Markov models
In many problems involving multivariate time series, hidden Markov models (HMMs) are
often employed for modeling complex behavior over time. HMMs can, however, require …
often employed for modeling complex behavior over time. HMMs can, however, require …
Approximate inference for dynamic Bayesian networks: sliding window approach
XG Gao, JF Mei, HY Chen, DQ Chen - Applied intelligence, 2014 - Springer
Abstract Dynamic Bayesian networks (DBNs) are probabilistic graphical models that have
become a ubiquitous tool for compactly describing statistical relationships among a group of …
become a ubiquitous tool for compactly describing statistical relationships among a group of …
Value‐based potentials: Exploiting quantitative information regularity patterns in probabilistic graphical models
When dealing with complex models (ie, models with many variables, a high degree of
dependency between variables, or many states per variable), the efficient representation of …
dependency between variables, or many states per variable), the efficient representation of …
[HTML][HTML] Probabilistic inference with noisy-threshold models based on a CP tensor decomposition
J Vomlel, P Tichavský - International Journal of Approximate Reasoning, 2014 - Elsevier
The specification of conditional probability tables (CPTs) is a difficult task in the construction
of probabilistic graphical models. Several types of canonical models have been proposed to …
of probabilistic graphical models. Several types of canonical models have been proposed to …
Learning recursive probability trees from probabilistic potentials
A Recursive Probability Tree (RPT) is a data structure for representing the potentials
involved in Probabilistic Graphical Models (PGMs). This structure is developed with the aim …
involved in Probabilistic Graphical Models (PGMs). This structure is developed with the aim …
Using Value-Based Potentials for Making Approximate Inference on Probabilistic Graphical Models
The computerization of many everyday tasks generates vast amounts of data, and this has
lead to the development of machine-learning methods which are capable of extracting …
lead to the development of machine-learning methods which are capable of extracting …