Results 1  10
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85
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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to the correct marginals. However, on the QMR network, the loopy be liefs oscillated and had no obvious relation ship to the correct posteriors. We present some initial investigations into the cause of these oscillations, and show that some sim ple methods of preventing them lead to the wrong results
Being Bayesian about network structure
 Machine Learning
, 2000
"... Abstract. In many multivariate domains, we are interested in analyzing the dependency structure of the underlying distribution, e.g., whether two variables are in direct interaction. We can represent dependency structures using Bayesian network models. To analyze a given data set, Bayesian model sel ..."
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Cited by 299 (3 self)
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is smaller and more regular than the space of structures, and has much a smoother posterior “landscape”. We present empirical results on synthetic and reallife datasets that compare our approach to full model averaging (when possible), to MCMC over network structures, and to a nonBayesian bootstrap
The simulation smoother for time series models
 BIOMETRIKA (1995), 82,2, PP. 33950
, 1995
"... Recently suggested procedures for simulating from the posterior density of states given a Gaussian state space time series are refined and extended. We introduce and study the simulation smoother, which draws from the multivariate posterior distribution of the disturbances of the model, so avoiding ..."
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Cited by 215 (17 self)
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Recently suggested procedures for simulating from the posterior density of states given a Gaussian state space time series are refined and extended. We introduce and study the simulation smoother, which draws from the multivariate posterior distribution of the disturbances of the model, so avoiding
Smoothers for discontinuous signals
 J. Nonpar. Statist
, 2002
"... First we explain the interplay between robust loss functions, nonlinear lters and Bayes smoothers for edgepreserving image reconstruction. Then we prove the surprising fact that maximum posterior smoothers are nonlinear lters. A (generalized) Potts prior for segmentation and piecewise smoothing of ..."
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Cited by 27 (8 self)
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First we explain the interplay between robust loss functions, nonlinear lters and Bayes smoothers for edgepreserving image reconstruction. Then we prove the surprising fact that maximum posterior smoothers are nonlinear lters. A (generalized) Potts prior for segmentation and piecewise smoothing
An Ensemble Smoother with Error Estimates
, 2001
"... A smoother introduced earlier by van Leeuwen and Evensen is applied to a problem in which real observations are used in an area with strongly nonlinear dynamics. The derivation is new, but it resembles an earlier derivation by van Leeuwen and Evensen. Again a Bayesian view is taken in which the pr ..."
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Cited by 18 (2 self)
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A smoother introduced earlier by van Leeuwen and Evensen is applied to a problem in which real observations are used in an area with strongly nonlinear dynamics. The derivation is new, but it resembles an earlier derivation by van Leeuwen and Evensen. Again a Bayesian view is taken in which
Fast Particle Smoothers
"... At the heart of many statistical problems is the calculation of smoothed posterior distribution, which describes the uncertainty associated with a state, on condition of data from the past, the present and the future. In general, it becomes analytically intractable when the state space is highdimen ..."
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At the heart of many statistical problems is the calculation of smoothed posterior distribution, which describes the uncertainty associated with a state, on condition of data from the past, the present and the future. In general, it becomes analytically intractable when the state space is high
A FixedLag Particle Smoother for Blind SISO Equalization of TimeVarying Channels
"... Abstract—We introduce a new sequential importance sampling (SIS) algorithm which propagates in time a Monte Carlo approximation of the posterior fixedlag smoothing distribution of the symbols under doublyselective channels. We perform an exact evaluation of the optimal importance distribution, at ..."
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Cited by 1 (0 self)
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Abstract—We introduce a new sequential importance sampling (SIS) algorithm which propagates in time a Monte Carlo approximation of the posterior fixedlag smoothing distribution of the symbols under doublyselective channels. We perform an exact evaluation of the optimal importance distribution
Parallel Metropolis coupled Markov chain Monte Carlo for Bayesian phylogenetic inference. Bioinformatics
, 2004
"... Motivation: Bayesian estimation of phylogeny is based on the posterior probability distribution of trees. Currently, the only numerical method that can effectively approximate posterior probabilities of trees is Markov chain Monte Carlo (MCMC). Standard implementations of MCMC can be prone to entrap ..."
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Cited by 73 (0 self)
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Motivation: Bayesian estimation of phylogeny is based on the posterior probability distribution of trees. Currently, the only numerical method that can effectively approximate posterior probabilities of trees is Markov chain Monte Carlo (MCMC). Standard implementations of MCMC can be prone
RNA MultiStructure Landscapes  A Study Based on Temperature Dependent Partition Functions
"... Statistical properties of RNA folding landscapes obtained by the partition function algorithm (McCaskill, 1990) are investigated in detail. The pair correlation of free energies as a function of the Hamming distance is used as a measure for the ruggedness of the landscape. The calculation of the par ..."
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Cited by 41 (9 self)
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and pair correlations at the level of the structures themselves are computed. Just as with landscapes based on most stable secondary structure prediction, the landscapes defined on the full biophysical GCAU alphabet are much smoother than the landscapes restricted to pure GC sequences and the correlation
Novelty and familiarity activations in PET studies of memory encoding and retrieval.
 Cereb. Cortex
, 1996
"... Nine young righthanded men viewed colored pictures of people, scenes, and landscapes. Then, 24 hr later while undergoing PET scanning, they viewed previously studied (OLD) pictures in one type of scan, and previously not seen (NEW) pictures in another. The OLDNEW subtraction of PET images indicat ..."
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Cited by 82 (5 self)
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Nine young righthanded men viewed colored pictures of people, scenes, and landscapes. Then, 24 hr later while undergoing PET scanning, they viewed previously studied (OLD) pictures in one type of scan, and previously not seen (NEW) pictures in another. The OLDNEW subtraction of PET images
Results 1  10
of
85