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Belief Propagation
, 2010
"... When a pair of nuclearpowered Russian submarines was reported patrolling off the eastern seaboard of the U.S. last summer, Pentagon officials expressed wariness over the Kremlin’s motivations. At the same time, these officials emphasized their confidence in the U.S. Navy’s tracking capabilities: “W ..."
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Cited by 479 (11 self)
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When a pair of nuclearpowered Russian submarines was reported patrolling off the eastern seaboard of the U.S. last summer, Pentagon officials expressed wariness over the Kremlin’s motivations. At the same time, these officials emphasized their confidence in the U.S. Navy’s tracking capabilities: “We’ve known where they were,” a senior Defense Department official told the New York Times, “and we’re not concerned about our ability to track the subs.” While the official did not divulge the methods used by the Navy to track submarines, the Times added that such
The Transferable Belief Model
 ARTIFICIAL INTELLIGENCE
, 1994
"... We describe the transferable belief model, a model for representing quantified beliefs based on belief functions. Beliefs can be held at two levels: (1) a credal level where beliefs are entertained and quantified by belief functions, (2) a pignistic level where beliefs can be used to make decisions ..."
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Cited by 486 (15 self)
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We describe the transferable belief model, a model for representing quantified beliefs based on belief functions. Beliefs can be held at two levels: (1) a credal level where beliefs are entertained and quantified by belief functions, (2) a pignistic level where beliefs can be used to make decisions
Loopy Belief Propagation for Approximate Inference: An Empirical Study
 In Proceedings of Uncertainty in AI
, 1999
"... Recently, researchers have demonstrated that "loopy belief propagation"  the use of Pearl's polytree algorithm in a Bayesian network with loops  can perform well in the context of errorcorrecting codes. The most dramatic instance of this is the near Shannonlimit performa ..."
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Cited by 680 (18 self)
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Recently, researchers have demonstrated that "loopy belief propagation"  the use of Pearl's polytree algorithm in a Bayesian network with loops  can perform well in the context of errorcorrecting codes. The most dramatic instance of this is the near Shannon
Fusion, Propagation, and Structuring in Belief Networks
 ARTIFICIAL INTELLIGENCE
, 1986
"... Belief networks are directed acyclic graphs in which the nodes represent propositions (or variables), the arcs signify direct dependencies between the linked propositions, and the strengths of these dependencies are quantified by conditional probabilities. A network of this sort can be used to repre ..."
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Cited by 482 (8 self)
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Belief networks are directed acyclic graphs in which the nodes represent propositions (or variables), the arcs signify direct dependencies between the linked propositions, and the strengths of these dependencies are quantified by conditional probabilities. A network of this sort can be used
DNNFbased belief state estimation
 in Proc. of AAAI’06
, 2006
"... As embedded systems grow increasingly complex, there is a pressing need for diagnosing and monitoring capabilities that estimate the system state robustly. This paper is based on approaches that address the problem of robustness by reasoning over declarative models of the physical plant, represent ..."
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Cited by 8 (1 self)
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sented as a variant of factored Hidden Markov Models, called Probabilistic Concurrent Constraint Automata. Prior work on Mode Estimation of PCCAs is based on a BestFirst Trajectory Enumeration (BFTE) algorithm. Two algorithms have since made improvements to the BFTE algorithm: 1) the BestFirst Belief State
On the Revision of Probabilistic Belief States
 Notre Dame Journal of Formal Logic
, 1995
"... In this paper we describe two approaches to the revision of probability functions. We assume that a probabilistic state of belief is captured by a counterfactual probability or Popper function, the revision of which determines a new Popper function. We describe methods whereby the original function ..."
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Cited by 5 (1 self)
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In this paper we describe two approaches to the revision of probability functions. We assume that a probabilistic state of belief is captured by a counterfactual probability or Popper function, the revision of which determines a new Popper function. We describe methods whereby the original function
Reinforcement Learning Using Approximate Belief States
, 1999
"... The problem of developing good policies for partially observable Markov decision problems (POMDPs) remains one of the most challenging areas of research in stochastic planning. One line of research in this area involves the use of reinforcement learning with belief states, probability distributi ..."
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Cited by 12 (0 self)
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The problem of developing good policies for partially observable Markov decision problems (POMDPs) remains one of the most challenging areas of research in stochastic planning. One line of research in this area involves the use of reinforcement learning with belief states, probability
Valuedirected belief state approximation for pomdps
 In UAI2000
, 2000
"... We consider the problem beliefstate monitoring for the purposes of implementing a policy for a partiallyobservable Markov decision process (POMDP), specifically how one might approximate the belief state. Other schemes for beliefstate approximation (e.g., based on minimizing a measure such as KLd ..."
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Cited by 27 (3 self)
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We consider the problem beliefstate monitoring for the purposes of implementing a policy for a partiallyobservable Markov decision process (POMDP), specifically how one might approximate the belief state. Other schemes for beliefstate approximation (e.g., based on minimizing a measure such as KL
Selfdiscrepancy: A theory relating self and affect
 Psychological Review
, 1987
"... This article presents a theory of how different types of discrepancies between selfstate representations are related to different kinds of emotional vulnerabilities. One domain of the self (actual; ideal; ought) and one standpoint on the self (own; significant other) constitute each type of selfs ..."
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Cited by 567 (7 self)
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the actual/own selfstate and ought selfstates (i.e., representations of an individual's beliefs about his or her own or a significant other's beliefs about the individual's duties, responsibilities, or obligations) signify the presence of negative outcomes, which is associated
A Bayesian method for the induction of probabilistic networks from data
 MACHINE LEARNING
, 1992
"... This paper presents a Bayesian method for constructing probabilistic networks from databases. In particular, we focus on constructing Bayesian belief networks. Potential applications include computerassisted hypothesis testing, automated scientific discovery, and automated construction of probabili ..."
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Cited by 1381 (32 self)
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This paper presents a Bayesian method for constructing probabilistic networks from databases. In particular, we focus on constructing Bayesian belief networks. Potential applications include computerassisted hypothesis testing, automated scientific discovery, and automated construction
Results 1  10
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