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A Region Inference Algorithm
 ACM TRANSACTIONS ON PROGRAMMING LANGUAGES AND SYSTEMS
, 1998
"... This article presents an algorithm which implements the specification. We prove that the algorithm is sound with respect to the region inference rules and that it always terminates even though the region inference rules permit polymorphic recursion in regions. The algorithm is the result of several ..."
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Cited by 88 (4 self)
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This article presents an algorithm which implements the specification. We prove that the algorithm is sound with respect to the region inference rules and that it always terminates even though the region inference rules permit polymorphic recursion in regions. The algorithm is the result of several
Inference Algorithms for Similarity Networks
 in Uncertainty in Artificial Intelligence: Proceedings of the Ninth Conference
, 1993
"... We examine two types of similarity networks each based on a distinct notion of relevance. For both types of similarity networks we present an efficient inference algorithm that works under the assumption that every event has a nonzero probability of occurrence. Another inference algorithm is develop ..."
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Cited by 1 (0 self)
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We examine two types of similarity networks each based on a distinct notion of relevance. For both types of similarity networks we present an efficient inference algorithm that works under the assumption that every event has a nonzero probability of occurrence. Another inference algorithm
Inference algorithms analysis
, 2011
"... Boolean network tomography establishes relationships between the congestion status of network links and those of endtoend paths. These relationships are used by inference algorithms to determine which network links are congested at any point in time. Our work shows that no stateoftheart infere ..."
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Cited by 2 (2 self)
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Boolean network tomography establishes relationships between the congestion status of network links and those of endtoend paths. These relationships are used by inference algorithms to determine which network links are congested at any point in time. Our work shows that no state
Dynamic Bayesian Networks: Representation, Inference and Learning
, 2002
"... Modelling sequential data is important in many areas of science and engineering. Hidden Markov models (HMMs) and Kalman filter models (KFMs) are popular for this because they are simple and flexible. For example, HMMs have been used for speech recognition and biosequence analysis, and KFMs have bee ..."
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Cited by 770 (3 self)
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sequential data.
In particular, the main novel technical contributions of this thesis are as follows: a way of representing
Hierarchical HMMs as DBNs, which enables inference to be done in O(T) time instead of O(T 3), where T is the length of the sequence; an exact smoothing algorithm that takes O(log T
Bayesian Inference Algorithm on Raw
"... This work explores the performance of Raw, a parallel hardware platform developed at MIT, running a Bayesian inference algorithm. Motivation for examining this parallel system is a growing interest in creating a selflearning and cognitive processor, which these hardware and software components can ..."
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This work explores the performance of Raw, a parallel hardware platform developed at MIT, running a Bayesian inference algorithm. Motivation for examining this parallel system is a growing interest in creating a selflearning and cognitive processor, which these hardware and software components can
ANALYSES OF HAPLOTYPE INFERENCE ALGORITHMS
"... We present experimental and theoretical analyses of data requirements for haplotype inference algorithms. Our experiments include a broad range of problem sizes under two standard models of tree distribution and were designed to yield statistically robust results despite the size of the sample s ..."
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We present experimental and theoretical analyses of data requirements for haplotype inference algorithms. Our experiments include a broad range of problem sizes under two standard models of tree distribution and were designed to yield statistically robust results despite the size of the sample
Variational algorithms for approximate Bayesian inference
, 2003
"... The Bayesian framework for machine learning allows for the incorporation of prior knowledge in a coherent way, avoids overfitting problems, and provides a principled basis for selecting between alternative models. Unfortunately the computations required are usually intractable. This thesis presents ..."
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Cited by 440 (9 self)
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a unified variational Bayesian (VB) framework which approximates these computations in models with latent variables using a lower bound on the marginal likelihood. Chapter 1 presents background material on Bayesian inference, graphical models, and propagation algorithms. Chapter 2 forms
Graphical models, exponential families, and variational inference
, 2008
"... The formalism of probabilistic graphical models provides a unifying framework for capturing complex dependencies among random variables, and building largescale multivariate statistical models. Graphical models have become a focus of research in many statistical, computational and mathematical fiel ..."
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Cited by 819 (28 self)
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likelihoods, marginal probabilities and most probable configurations. We describe how a wide varietyof algorithms — among them sumproduct, cluster variational methods, expectationpropagation, mean field methods, maxproduct and linear programming relaxation, as well as conic programming relaxations — can
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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limit performance of "Turbo Codes" codes whose decoding algorithm is equivalent to loopy belief propagation in a chainstructured Bayesian network. In this paper we ask: is there something spe cial about the errorcorrecting code context, or does loopy propagation work as an ap proximate inference scheme
Results 1  10
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