Bayesian Networks Parameter Learning Based on Noise Data Smoothing in Missing Information

2012 ◽  
Author(s):  
Ren Jia ◽  
Tang Tao ◽  
Yuan Ying
Keyword(s):  
Bayesian Networks ◽  
Parameter Learning ◽  
Missing Information ◽  
Data Smoothing ◽  
Noise Data

scholarly journals Parameter Learning of Logic Programs for Symbolic-Statistical Modeling

2001 ◽  
Vol 15 ◽  
pp. 391-454 ◽  
Author(s):  
T. Sato ◽  
Y. Kameya
Keyword(s):  
Em Algorithm ◽  
Bayesian Networks ◽  
Complexity Analysis ◽  
Statistical Parameter ◽  
Research Field ◽  
Mathematical Framework ◽  
Parameter Learning ◽  
Sequential Decision ◽  
Logic Programs ◽  
Possible World

We propose a logical/mathematical framework for statistical parameter learning of parameterized logic programs, i.e. definite clause programs containing probabilistic facts with a parameterized distribution. It extends the traditional least Herbrand model semantics in logic programming to distribution semantics, possible world semantics with a probability distribution which is unconditionally applicable to arbitrary logic programs including ones for HMMs, PCFGs and Bayesian networks. We also propose a new EM algorithm, the graphical EM algorithm, that runs for a class of parameterized logic programs representing sequential decision processes where each decision is exclusive and independent. It runs on a new data structure called support graphs describing the logical relationship between observations and their explanations, and learns parameters by computing inside and outside probability generalized for logic programs. The complexity analysis shows that when combined with OLDT search for all explanations for observations, the graphical EM algorithm, despite its generality, has the same time complexity as existing EM algorithms, i.e. the Baum-Welch algorithm for HMMs, the Inside-Outside algorithm for PCFGs, and the one for singly connected Bayesian networks that have been developed independently in each research field. Learning experiments with PCFGs using two corpora of moderate size indicate that the graphical EM algorithm can significantly outperform the Inside-Outside algorithm.

Recognition of Human Intentions Using Bayesian Artificial Intelligence

2007 ◽  
Author(s):  
R. Tse ◽  
G. Seet ◽  
S. K. Sim
Keyword(s):  
Artificial Intelligence ◽  
Maximum Likelihood ◽  
Bayesian Networks ◽  
Success Rate ◽  
Dynamic Bayesian Networks ◽  
Laser Sensor ◽  
Parameter Learning ◽  
Complete Set ◽  
Bayesian Artificial Intelligence

Controlling a robot to perform a task is more difficult than commanding a human. A robot needs to be preprogrammed to perform a task. This is achieved by providing the robot with a complete set of step-by-step commands from the beginning till the end. In contrast, to a human, recalling an experience when he was instructed with the same command in a similar situation, a human would be able to guess what intention behind such a command is and could then behave cooperatively. Our objective is to equip the robot with such a capability of recognizing some simple human intentions required of a robot, such as: moving around a corner, moving parallel to the wall, or moving towards an object. The cues used by the robot to make an inference were: the odometer and laser sensor readings, and the human operator’s commands given. Using the Maximum-Likelihood (ML) parameter learning on Dynamic Bayesian Networks, the correlations between these cues and the intentions were modeled and used to infer the human intentions in controlling the robot. From the experiments, the robot was able to learn and infer the above mentioned intentions of the human user with a satisfying success rate.

Decision Making Through the Application of Bayesian Network for Internal Corrosion Assessment of Pipelines

2018 ◽  
Author(s):  
Francois Ayello ◽  
Guanlan Liu ◽  
Jiana Zhang
Keyword(s):  
Decision Making ◽  
Bayesian Networks ◽  
Value Of Information ◽  
Operating Conditions ◽  
Final Decision ◽  
Missing Information ◽  
Internal Corrosion ◽  
Design Life ◽  
Corrosion Risk ◽  
Pipeline Failure

Decision making for a new pipeline’s design and provision of the most effective maintenance or repair measures for a pipeline in operation can be a long and costly process. The final decision made, whether during design or operation, may not always reduce the risk or remediate the threat. This is mainly due to the uncertainty and missing information regarding the field chemistry for current and future pipeline operating conditions, that were not considered and quantified during the assessment. In this paper, two case studies of pipeline internal corrosion risk are presented, one for pipeline in design and the latter for pipeline in operation. Both cases were assessed using Bayesian Networks. Bayesian Networks (BN) have been used to quantify the value of information of uncertain and missing data. BN displays the cause-effect relationships of these data in the form of conditional probabilities to describe how one’s data is influencing internal corrosion rates probability. Thus, predicting the pipeline’s conditions over the design life. Operators can visualize the development of internal corrosion within a pipeline over time and gain clearer understanding of the causal relationships that could lead to pipeline failure. The results allowed operators to confirm the effects of the parameter and followed by a sensitivity analysis to find out which data to prioritize in acquisition and validation before proceeding to decide on how the pipeline should be designed and maintained/inspected in future.

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