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Sequential Diagnosis in the Independence Bayesian Framework [chapter]

David McSherry
2002 Lecture Notes in Computer Science  
We present a new approach to test selection in sequential diagnosis (or classification) in the independence Bayesian framework that resembles the hypothetico-deductive approach to test selection used by  ...  In spite of its relative simplicity in comparison with previous models of hypotheticodeductive reasoning, the approach retains the advantage that the relevance of a selected test can be explained in strategic  ...  for sequential diagnosis in the independence Bayesian framework.  ... 
doi:10.1007/3-540-46019-5_17 fatcat:j5ijalj7enbivbbo4nhpioteli

Sequential diagnosis in the independence Bayesian framework

D. McSherry
2003 Soft Computing - A Fusion of Foundations, Methodologies and Applications  
We present a new approach to test selection in sequential diagnosis (or classification) in the independence Bayesian framework that resembles the hypothetico-deductive approach to test selection used by  ...  In spite of its relative simplicity in comparison with previous models of hypotheticodeductive reasoning, the approach retains the advantage that the relevance of a selected test can be explained in strategic  ...  for sequential diagnosis in the independence Bayesian framework.  ... 
doi:10.1007/s00500-002-0252-0 fatcat:jwtyblggrbcjxaf3kw5xqxkauq

Using Bayesian networks for root cause analysis in statistical process control

Adel Alaeddini, Ibrahim Dogan
2011 Expert systems with applications  
This network is then trained under the framework of Bayesian networks and a suggested data structure using process information and chart patterns.  ...  Despite their fame and capability in detecting out-of-control conditions, control charts are not effective tools for fault diagnosis.  ...  Dey and Stori (2005) use data from multiple sensors on sequential machining operations through a causal belief network framework to provide a probabilistic diagnosis of the root cause of the process  ... 
doi:10.1016/j.eswa.2011.02.171 fatcat:jfbjidp6wnhodl3boupc23o6om

Discussion on "Optimal Sequential Surveillance for Finance, Public Health, and Other Areas" by Marianne Frisén

Tze Leung Lai, Haipeng Xing
2009 Sequential Analysis  
unknown pre-and post-change parameters and multiple change-points in passive surveillance.  ...  We give some comments and supplementary results on Professor Frisén's comprehensive survey of sequential surveillance and its applications.  ...  ACKNOWLEDGMENT Lai's research is supported by the National Science Foundation, under grant DMS-0805879.  ... 
doi:10.1080/07474940903041688 fatcat:l2pvxycthbh7xhguitjy25npue

Special Issue on causal networks: papers from the Second CaNew Workshop

Ramón Sangüesa, Ulises Cortés
2001 International Journal of Approximate Reasoning  
Feature subset selection by Bayesian networks: a comparison with genetic and sequential algorithms by Inza, Larrañaga and Sierra tries to analyze the bene®ts of using Bayesian networks in estimation distribution  ...  This shows a way for applying Bayesian networks in such a muchdemanded task as diagnosis is.  ... 
doi:10.1016/s0888-613x(01)00035-4 fatcat:dgvnq5bw35bilfrcarfvkenaji

BAYESIAN METHODS FOR CONTROL LOOP MONITORING AND DIAGNOSIS

Biao Huang
2007 IFAC Proceedings Volumes  
As the backbone of the proposed framework, the emerging Bayesian methods are introduced and shown to be the appropriate tools.  ...  The new framework possesses a number of desired properties including, for example, probabilistic diagnosing procedure, flexibility in synthesizing different monitoring technologies, robustness in the presence  ...  The dynamic Bayesian inference derived above can be used to make sequential sensor diagnosis. As an example, let the process gain status be of main interest for the diagnosis.  ... 
doi:10.3182/20070606-3-mx-2915.00004 fatcat:rjicypjmqvfmbhfpdphulysdfe

Bayesian methods for control loop monitoring and diagnosis

Biao Huang
2008 Journal of Process Control  
As the backbone of the proposed framework, the emerging Bayesian methods are introduced and shown to be the appropriate tools.  ...  The new framework possesses a number of desired properties including, for example, probabilistic diagnosing procedure, flexibility in synthesizing different monitoring technologies, robustness in the presence  ...  The dynamic Bayesian inference derived above can be used to make sequential sensor diagnosis. As an example, let the process gain status be of main interest for the diagnosis.  ... 
doi:10.1016/j.jprocont.2008.06.006 fatcat:ji5pw6m3snbajogtuqsx2b2xxm

Forthcoming Papers

2005 Artificial Intelligence  
Cordier, A formal framework for the decentralised diagnosis of large scale discrete event systems and its application to telecommunication networks W. van der Hoek and M.  ...  Our algorithms and their weighted modifications are favorably tested against other methods in the literature, producing a sensible improvement in performance on most of the standard statistical databases  ...  A notion that has been suggested in the literature to facilitate Bayesian-network development is causal independence.  ... 
doi:10.1016/j.artint.2005.01.001 fatcat:leaob2jalbddpjizbpelscvpuy

Learning Continuous Time Bayesian Network Classifiers Using MapReduce

Marco Rossetti, Simone Villa
2014 Journal of Statistical Software  
This paper describes an efficient scalable parallel algorithm for parameter and structural learning in the case of complete data using the MapReduce framework.  ...  Details of the proposed algorithm are presented using Hadoop, an open-source implementation of a distributed file system and the MapReduce framework for distributed data processing.  ...  Acknowledgments The authors acknowledge the many helpful suggestions of anonymous referees which helped to improve the paper clarity and quality.  ... 
doi:10.18637/jss.v062.i03 fatcat:3g7hjtyksjhbvj2mmtfljtrr34

Adaptive Diagnosis in Distributed Systems

I. Rish, M. Brodie, S. Ma, N. Odintsova, A. Beygelzimer, G. Grabarnik, K. Hernandez
2005 IEEE Transactions on Neural Networks  
Finally, we discuss how to model the system's dynamics using Dynamic Bayesian networks, and an efficient approximate approach called sequential multifault; empirical results demonstrate clear advantage  ...  Index Terms-Diagnosis, probabilistic inference, Bayesian networks, information gain, computer networks, distributed systems, end-to-end transactions.  ...  We also thank EPP team, especially Herb Lee, Andy Frenkiel, and Marius Sabbath, for their collaboration on RAIL/EPP framework, and Douglas Griswold, Luis Moss, and Justin Ellis for providing the data and  ... 
doi:10.1109/tnn.2005.853423 pmid:16252819 fatcat:dtsmkddfejcbrgxauq3sejvumq

ECG signal enhancement using adaptive Kalman filter and signal averaging

M.H. Moradi, M. Ashoori Rad, R. Baghbani Khezerloo
2014 International Journal of Cardiology  
In addition, the sequential nature of the estimation problem motivates the use of a Bayesian framework in which the prior probability distribution assigned to the unknown parameters is updated every time  ...  This filter is derived using a Bayesian framework and constitutes a Kalman filter in which the dynamic variations in the ECG are modeled by a covariance matrix that is adaptively estimated every time new  ...  In addition, the sequential nature of the estimation problem motivates the use of a Bayesian framework in which the prior probability distribution assigned to the unknown parameters is updated every time  ... 
doi:10.1016/j.ijcard.2014.03.128 pmid:24717324 fatcat:hludceuwnfctjo5hirkw3sa2dy

A Bayesian decision support sequential model for severity of illness predictors and intensive care admissions in pneumonia

Amado Alejandro Baez, Laila Cochon, Jose Maria Nicolas
2019 BMC Medical Informatics and Decision Making  
Our objective was to assess the predictive value on critical illness and disposition of a sequential Bayesian Model that integrates Lactate and procalcitonin (PCT) for pneumonia.  ...  Community-acquired pneumonia (CAP) is one of the leading causes of morbidity and mortality in the USA.  ...  The Bayesian statistical model demonstrated a superior diagnostic gains in predicting ICU admissions with the independent integration of lactate compared to Procalcitonin.  ... 
doi:10.1186/s12911-019-1015-5 pmid:31888590 pmcid:PMC6937994 fatcat:z733tfs54jc2nia2ai3exiwt2y

Data assimilation and multisource decision-making in systems biology based on unobtrusive Internet-of-Things devices

Wei-Hua Tang, Wen-Hsien Ho, Yenming J Chen
2018 BioMedical Engineering OnLine  
Moreover, the required technologies are ready to support the desired disease diagnosis levels, such as hypothesis test, multiple evidence fusion, machine learning, data assimilation, and systems biology  ...  that can overcome the limitations of devices in terms of quality measurement.  ...  Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Published: 6 November 2018  ... 
doi:10.1186/s12938-018-0574-5 pmid:30396337 pmcid:PMC6218968 fatcat:dwn3n4shazhapiorqnzz4nkzna

Dirichlet enhanced relational learning

Zhao Xu, Volker Tresp, Kai Yu, Shipeng Yu, Hans-Peter Kriegel
2005 Proceedings of the 22nd international conference on Machine learning - ICML '05  
The experiments show that the additional flexibility in a nonparametric hierarchical Bayes approach results in a more accurate model of the dependencies between procedures and diagnosis and gives significantly  ...  Flexibility is added in a nonparametric hierarchical Bayesian approach, such that the learned knowledge can be truthfully represented.  ...  We apply our framework in the context of a medical data base. The entities in the model are hospitals, patients, diagnosis and procedures.  ... 
doi:10.1145/1102351.1102478 dblp:conf/icml/XuTYYK05 fatcat:yj4bffblmfhwdknayez6xscxfy

Bayesian joint models for longitudinal and survival data [article]

Carmen Armero
2020 arXiv   pre-print
A general formulation for BJM is examined in terms of the sampling distribution of the longitudinal and survival processes, the conditional distribution of the random effects and the prior distribution  ...  Next a basic BJM defined in terms of a mixed linear model and a Cox survival regression models is discussed and some extensions and other Bayesian topics are briefly outlined.  ...  Model diagnosis and model selection in BJM are very relevant issues that have been given little attention.  ... 
arXiv:2005.12822v1 fatcat:ka72zfhboffyxcb4nt2i5ukxli
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