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13
A Bayesian Analysis of Simulation Algorithms for Inference in Belief Networks,
 Networks
, 1993
"... A belief network is a graphical representation of the underlying probabilistic relationships in a complex system. Belief networks have been employed as a representation of uncertain relationships in computerbased diagnostic systems. These diagnostic systems provide assistance by assigning likeli ..."
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Cited by 17 (3 self)
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A belief network is a graphical representation of the underlying probabilistic relationships in a complex system. Belief networks have been employed as a representation of uncertain relationships in computerbased diagnostic systems. These diagnostic systems provide assistance by assigning likelihoods to alternative explanatory hypotheses in response to a set of findings or observations. Approximation algorithms have been used to compute likelihoods of hypotheses in large networks. We analyze the performance of leading Monte Carlo approximation algorithms for computing posterior probabilities in belief networks. The analysis differs from earlier attempts to characterize the behavior of simulation algorithms in our explicit use of Bayesian statistics: We update a probability distribution over target probabilities of interest with information from randomized trials. For real ffl; ffi ! 1 and for a probabilistic inference Pr[xje], the output of an inference approximation algorithm is an (ffl; ffi)estimate of Pr[xje] if with probability at least 1 \Gamma ffi the output is within relative error ffl of Pr[xje]. We construct a stopping rule for the number of simulations required by logic sampling, randomized approximation schemes, and likelihood weighting to provide (ffl; ffi)estimates of Pr[xje]. With probability 1 \Gamma ffi, the stopping rule is optimal in the sense that the algorithm performs the minimum number of required simulations. We prove that our stopping rules are insensitive to the prior probability distribution on Pr[xje].
Learning stochastic feedforward networks
, 1990
"... Introduction The work reported here began with the desire to find a network architecture that shared with Boltzmann machines [6, 1, 7] the capacity to learn arbitrary probability distributions over binary vectors, but that did not require the negative phase of Boltzmann machine learning. It was hypo ..."
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Cited by 12 (1 self)
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Introduction The work reported here began with the desire to find a network architecture that shared with Boltzmann machines [6, 1, 7] the capacity to learn arbitrary probability distributions over binary vectors, but that did not require the negative phase of Boltzmann machine learning. It was hypothesized that eliminating the negative phase would improve learning performance. This goal was achieved by replacing the Boltzmann machine's symmetric connections with feedforward connections. In analogy with Boltzmann machines, the sigmoid function was used to compute the conditional probability of a unit being on from the weighted input from other units. Stochastic simulation of such a network is somewhat more complex than for a Boltzmann machine, but is still possible using local communication. Maximum likelihood, gradientascent learning can be done with a local Hebbtype rule.
Stochastic Sampling and Search in Belief Updating Algorithms for . . .
 IN WORKING NOTES OF THE AAAI SPRING SYMPOSIUM ON SEARCH TECHNIQUES FOR PROBLEM SOLVING UNDER UNCERTAINTY AND INCOMPLETE INFORMATION
, 1999
"... Bayesian networks are gaining an increasing popularity as a modeling tool for complex problems involving reasoning under uncertainty. Since belief updating in very large Bayesian networks cannot be e#ectively addressed by exact methods, approximate inference schemes may be often the only comput ..."
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Cited by 8 (1 self)
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Bayesian networks are gaining an increasing popularity as a modeling tool for complex problems involving reasoning under uncertainty. Since belief updating in very large Bayesian networks cannot be e#ectively addressed by exact methods, approximate inference schemes may be often the only computationally feasible alternative. There are two basic classes of approximate schemes: stochastic sampling and searchbased algorithms. We summarize
Decision Analytic Networks in Artificial Intelligence
, 1995
"... Researchers in artificial intelligence and decision analysis share a concern with the construction of formal models of human knowledge and expertise. Historically, however, their approaches to these problems have diverged. Members of these two communities have recently discovered common ground: a fa ..."
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Cited by 7 (0 self)
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Researchers in artificial intelligence and decision analysis share a concern with the construction of formal models of human knowledge and expertise. Historically, however, their approaches to these problems have diverged. Members of these two communities have recently discovered common ground: a family of graphical models of decision theory known as influence diagrams or as belief networks. These models are equally attractive to theoreticians, decision modelers, and designers of knowledgebased systems. From a theoretical perspective, they combine graph theory, probability theory and decision theory. From an implementation perspective, they lead to powerful automated systems. Although many practicing decision analysts have already adopted influence diagrams as modeling and structuring tools, they may remain unaware of the theoretical work that has emerged from the artificial intelligence community. This paper surveys the first decade or so of this work. Investment Technology Group, ...
Efficient SearchBased Inference for NoisyOR Belief Networks: TopEpsilon
 In Proc. Twelfth Conf. on Uncertainty in Artificial Intelligence
, 1996
"... Inference algorithms for arbitrary belief networks are impractical for large, complex belief networks. ..."
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Cited by 6 (0 self)
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Inference algorithms for arbitrary belief networks are impractical for large, complex belief networks.
Bayesian Network Models for Generation of Crisis Management Training Scenarios
 In Proceedings of IAAI98
, 1998
"... We present a noisyOR Bayesian network model for simulationbased training, and an efficient searchbased algorithm for automatic synthesis of plausible training scenarios from constraint specifications. This randomized algorithm for approximate causal inference is shown to outperform other randomiz ..."
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Cited by 4 (4 self)
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We present a noisyOR Bayesian network model for simulationbased training, and an efficient searchbased algorithm for automatic synthesis of plausible training scenarios from constraint specifications. This randomized algorithm for approximate causal inference is shown to outperform other randomized methods, such as those based on perturbation of the maximally plausible scenario. It has the added advantage of being able to generate acceptable scenarios (based on a maximum penalized likelihood criterion) faster than human subject matter experts, and with greater diversity than deterministic inference. We describe a fieldtested interactive training system for crisis management and show how our model can be applied offline to produce scenario specifications. We then evaluate the performance of our automatic scenario generator and compare its results to those achieved by human instructors, stochastic simulation, and maximum likelihood inference. Finally, we discuss the applicability of our system and framework to a broader range of modeling problems for computerassisted instruction.
A Dynamic Bayesian Network for Handling Uncertainty in a Decision Support System Adapted to the Monitoring of Patients Treated by Hemodialysis
 in "17th IEEE International Conference on Tools with Artificial Intelligence  ICTAI’05, Hong Kong/China
, 2005
"... Telemedicine is a mean of facilitating the distribution of human resources and professional competences. It can speed up diagnosis and therapeutic care delivery and allow peripheral healthcare providers to receive continuous assistance from specialized centers. The need of specialized human resource ..."
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Cited by 3 (0 self)
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Telemedicine is a mean of facilitating the distribution of human resources and professional competences. It can speed up diagnosis and therapeutic care delivery and allow peripheral healthcare providers to receive continuous assistance from specialized centers. The need of specialized human resources becomes critical with the aging of the population. The treatment of renal failure is an example where telemedicine can help to increase care quality. Over the last decades Bayesian networks has become a popular representation for encoding uncertain expert knowledge. Dynamic Bayesian networks are an extension of Bayesian networks for modeling dynamic processes. We developed a dynamic Bayesian network adapted to the monitoring of the dry weight of patients suffering from chronic renal failure treated by hemodialysis. An experimentation conducted at dialysis units indicated that the system is reliable and gets the approbation of its users. 1.
Making decision research useful — not just rewarding
 Judgment and Decision Making
, 2006
"... An experienced decision aider reflects on how misaligned priorities produce decision research that is less useful than it could be. Scientific interest and professional standing may motivate researchers — and their funders and publishers — more powerfully than concern to help people make better deci ..."
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Cited by 3 (2 self)
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An experienced decision aider reflects on how misaligned priorities produce decision research that is less useful than it could be. Scientific interest and professional standing may motivate researchers — and their funders and publishers — more powerfully than concern to help people make better decisions.
An Adaptive Reasoning Approach Towards Efficient Ordering of Composite Hypotheses
, 1991
"... u! A Bayesian network is a knowledge representation framework for encoding both qualitative and quantitative probabilistic dependencies among a set of propositional (or random) variables. An important type of probabilistic inference in a Bayesian network is the derivation of the most probable compos ..."
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Cited by 2 (2 self)
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u! A Bayesian network is a knowledge representation framework for encoding both qualitative and quantitative probabilistic dependencies among a set of propositional (or random) variables. An important type of probabilistic inference in a Bayesian network is the derivation of the most probable composite hypotheses  a setof hypotheses composed of multiple variables in a network. Such a type of probabilistic inference, however, is computationally intractable. In this paper an adaptive reasoning approach based on qualitative interval arithmetic is proposed as a method of dealing with the computational problem. Using this approach, a qualitative boundary, which reflects the upper and lower limits of a posterior likelihood, can be derived for each composite hypothesis. The advantage of bounding each composite hypothesis qualitatively is that the quantitative values of the posterior likelihoods are not all necessary in the course of an inference. Consequently, an exhaustive evaluation can ...
An Adaptive Reasoning Approach for Ordering MultipleVariable Hypotheses
 Proc. of the fourth UNB Artificial Intelligence Symposium
, 1991
"... Identifying a list of the most likely multiplevariable hypotheses in a Bayesian network is an important type of query commonly encountered in many problem domains. Yet, it received little attention in the past due to, at least in part, the limited success in dealing with the complexity problem of o ..."
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Cited by 1 (1 self)
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Identifying a list of the most likely multiplevariable hypotheses in a Bayesian network is an important type of query commonly encountered in many problem domains. Yet, it received little attention in the past due to, at least in part, the limited success in dealing with the complexity problem of ordering exponential number of hypotheses. The objective of this research is to develop an efficient reasoning scheme for the derivation of a list of the most likely multiplevariable hypotheses. Various probabilistic properties will be explored in the development of such a reasoning scheme, so that current existing efficient algorithms for singlevariable hypotheses can be extended to cope with the partial ordering of multiplevariable hypotheses. The complexity issue is discussed and also several examples are used to illustrate the effectiveness of the reasoning scheme. I. Introduction Expert systems provide practical solutions to various problems of reasoning under conditions of uncertain...