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4,533
Probabilistic Inference Using Markov Chain Monte Carlo Methods
, 1993
"... Probabilistic inference is an attractive approach to uncertain reasoning and empirical learning in artificial intelligence. Computational difficulties arise, however, because probabilistic models with the necessary realism and flexibility lead to complex distributions over highdimensional spaces. R ..."
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Cited by 736 (24 self)
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Probabilistic inference is an attractive approach to uncertain reasoning and empirical learning in artificial intelligence. Computational difficulties arise, however, because probabilistic models with the necessary realism and flexibility lead to complex distributions over highdimensional spaces
A Neural Probabilistic Language Model
 JOURNAL OF MACHINE LEARNING RESEARCH
, 2003
"... A goal of statistical language modeling is to learn the joint probability function of sequences of words in a language. This is intrinsically difficult because of the curse of dimensionality: a word sequence on which the model will be tested is likely to be different from all the word sequences seen ..."
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Cited by 447 (19 self)
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training sentence to inform the model about an exponential number of semantically neighboring sentences. The model learns simultaneously (1) a distributed representation for each word along with (2) the probability function for word sequences, expressed in terms of these representations. Generalization
A Language Modeling Approach to Information Retrieval
, 1998
"... Models of document indexing and document retrieval have been extensively studied. The integration of these two classes of models has been the goal of several researchers but it is a very difficult problem. We argue that much of the reason for this is the lack of an adequate indexing model. This sugg ..."
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Cited by 1154 (42 self)
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Models of document indexing and document retrieval have been extensively studied. The integration of these two classes of models has been the goal of several researchers but it is a very difficult problem. We argue that much of the reason for this is the lack of an adequate indexing model
Loopy belief propagation for approximate inference: An empirical study. In:
 Proceedings of Uncertainty in AI,
, 1999
"... Abstract 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 performanc ..."
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Cited by 676 (15 self)
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runs. We assumed that all leaf nodes were observed and calculated the pos Figure 2: The structure of a toyQMR network. This is a bipartite structure where the conditional distributions of the leaves are noisyor's. The network shown represents one sample from randomly generated structures where
The Determinants of Credit Spread Changes.
 Journal of Finance
, 2001
"... ABSTRACT Using dealer's quotes and transactions prices on straight industrial bonds, we investigate the determinants of credit spread changes. Variables that should in theory determine credit spread changes have rather limited explanatory power. Further, the residuals from this regression are ..."
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Cited by 422 (2 self)
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changes. Below, we investigate the determinants of credit spread changes. From a contingentclaims, or noarbitrage standpoint, credit spreads obtain for two fundamental reasons: 1) there is a risk of default, and 2) in the event of default, the bondholder receives only a portion of the promised payments
Model Checking of Probabilistic and Nondeterministic Systems
, 1995
"... . The temporal logics pCTL and pCTL* have been proposed as tools for the formal specification and verification of probabilistic systems: as they can express quantitative bounds on the probability of system evolutions, they can be used to specify system properties such as reliability and performance. ..."
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Cited by 291 (13 self)
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. In this paper, we present modelchecking algorithms for extensions of pCTL and pCTL* to systems in which the probabilistic behavior coexists with nondeterminism, and show that these algorithms have polynomialtime complexity in the size of the system. This provides a practical tool for reasoning
Probabilistic Mental Models: A Brunswikian Theory of Confidence
 Psychological Review
, 1991
"... Research on people’s confidence in their general knowledge has to date produced two fairly stable effects, many inconsistent results, and no comprehensive theory. We propose such a comprehensive framework, the theory of probabilistic mental models (PMM theory). The theory (a) explains both the overc ..."
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Cited by 270 (29 self)
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Research on people’s confidence in their general knowledge has to date produced two fairly stable effects, many inconsistent results, and no comprehensive theory. We propose such a comprehensive framework, the theory of probabilistic mental models (PMM theory). The theory (a) explains both
Approximating the permanent
 SIAM J. Computing
, 1989
"... Abstract. A randomised approximation scheme for the permanent of a 01 matrix is presented. The task of estimating a permanent is reduced to that of almost uniformly generating perfect matchings in a graph; the latter is accomplished by simulating a Markov chain whose states are the matchings in the ..."
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Cited by 345 (26 self)
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matrices in some reasonable probabilistic model for 01 matrices of given density. For the approach sketched above to be computationally efficient, the Markov chain must be rapidly mixing: informally, it must converge in a short time to its stationary distribution. A major portion of the paper is devoted
Structural Matching in Computer Vision Using Probabilistic Reasoning
, 1995
"... easurement error distributions is dependent on the type of geometric feature, the measurement noise model and the nature of the unknown scenetomodel transformation: some examples are presented. A number of variations on the basic labelling algorithm are described, of which some have implications f ..."
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Cited by 201 (15 self)
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easurement error distributions is dependent on the type of geometric feature, the measurement noise model and the nature of the unknown scenetomodel transformation: some examples are presented. A number of variations on the basic labelling algorithm are described, of which some have implications
Compositional Reasoning on (Probabilistic) Contracts
"... In this paper, we focus on Assume/Guarantee contracts consisting in (i) a non deterministic model of components behaviour, and (ii) a stochastic and non deterministic model of systems faults. Two types of contracts capable of capturing reliability and availability properties are considered. We show ..."
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In this paper, we focus on Assume/Guarantee contracts consisting in (i) a non deterministic model of components behaviour, and (ii) a stochastic and non deterministic model of systems faults. Two types of contracts capable of capturing reliability and availability properties are considered. We show
Results 1  10
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4,533