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Computing Bayesian CramérRao bounds
"... Abstract — An efficient messagepassing algorithm for computing the Bayesian CramérRao bound (BCRB) for general estimation problems is presented. The BCRB is a lower bound on the mean squared estimation error. The algorithm operates on a cyclefree factor graph of the system at hand. It can be appl ..."
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Cited by 4 (0 self)
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Abstract — An efficient messagepassing algorithm for computing the Bayesian CramérRao bound (BCRB) for general estimation problems is presented. The BCRB is a lower bound on the mean squared estimation error. The algorithm operates on a cyclefree factor graph of the system at hand. It can
CramérRao type bounds for localization
 EURASIP Journal on Applied Signal Processing
"... for sensor networks. This paper studies the CramérRao lower bound (CRB) for two kinds of localization based on noisy range measurements. The first is Anchored Localization in which the estimated positions of at least 3 nodes are known in global coordinates. We show some basic invariances of the CRB ..."
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Cited by 1 (0 self)
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for sensor networks. This paper studies the CramérRao lower bound (CRB) for two kinds of localization based on noisy range measurements. The first is Anchored Localization in which the estimated positions of at least 3 nodes are known in global coordinates. We show some basic invariances
Factorization: Variational Algorithm and CramérRao Bound a
"... Matrix factorization is a popular method for collaborative prediction, where unknown ratings are predicted by user and item factor matrices which are determined to approximate a useritem matrix as their product. Bayesian matrix factorization is preferred over other methods for collaborative filteri ..."
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Matrix factorization is a popular method for collaborative prediction, where unknown ratings are predicted by user and item factor matrices which are determined to approximate a useritem matrix as their product. Bayesian matrix factorization is preferred over other methods for collaborative
Analytic and Asymptotic Analysis of Bayesian CramérRao Bound for Dynamical Phase . . .
, 2007
"... In this paper, we present a closedform expression of a Bayesian CramérRao lower bound for the estimation of a dynamical phase offset in a nondataaided BPSK transmitting context. This kind of bound is derived considering two different scenarios: a first expression is obtained in an offline conte ..."
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Cited by 22 (6 self)
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In this paper, we present a closedform expression of a Bayesian CramérRao lower bound for the estimation of a dynamical phase offset in a nondataaided BPSK transmitting context. This kind of bound is derived considering two different scenarios: a first expression is obtained in an off
Standard CramerRao bound CramerRao bound with nuisance parameter Bayesian CramerRao bound Other bounds
"... We assume y(n) = a(n)e2ipif0n + b(n), n = 0,...,N − 1 with y(n) : the received signal a(n) : a zeromean random process or a timevarying amplitude. b(n) : circular white Gaussian stationary additive noise. Goal: Estimating the frequency f0 in multiplicative and additive noise ..."
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We assume y(n) = a(n)e2ipif0n + b(n), n = 0,...,N − 1 with y(n) : the received signal a(n) : a zeromean random process or a timevarying amplitude. b(n) : circular white Gaussian stationary additive noise. Goal: Estimating the frequency f0 in multiplicative and additive noise
RESEARCH Open Access Multipath exploited Bayesian and CramérRao
"... bounds for singlesensor target localization ..."
Posterior CramérRao bounds for discretetime nonlinear filtering
 IEEE Trans. Signal Processing
, 1998
"... Abstract—A meansquare error lower bound for the discretetime nonlinear filtering problem is derived based on the Van Trees (posterior) version of the Cramér–Rao inequality. This lower bound is applicable to multidimensional nonlinear, possibly nonGaussian, dynamical systems and is more general tha ..."
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Cited by 178 (4 self)
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Abstract—A meansquare error lower bound for the discretetime nonlinear filtering problem is derived based on the Van Trees (posterior) version of the Cramér–Rao inequality. This lower bound is applicable to multidimensional nonlinear, possibly nonGaussian, dynamical systems and is more general
Estimating Continuous Distributions in Bayesian Classifiers
 In Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence
, 1995
"... When modeling a probability distribution with a Bayesian network, we are faced with the problem of how to handle continuous variables. Most previous work has either solved the problem by discretizing, or assumed that the data are generated by a single Gaussian. In this paper we abandon the normality ..."
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Cited by 489 (2 self)
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When modeling a probability distribution with a Bayesian network, we are faced with the problem of how to handle continuous variables. Most previous work has either solved the problem by discretizing, or assumed that the data are generated by a single Gaussian. In this paper we abandon
Sparse Bayesian Learning and the Relevance Vector Machine
, 2001
"... This paper introduces a general Bayesian framework for obtaining sparse solutions to regression and classication tasks utilising models linear in the parameters. Although this framework is fully general, we illustrate our approach with a particular specialisation that we denote the `relevance vec ..."
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Cited by 958 (5 self)
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This paper introduces a general Bayesian framework for obtaining sparse solutions to regression and classication tasks utilising models linear in the parameters. Although this framework is fully general, we illustrate our approach with a particular specialisation that we denote the `relevance
Results 1  10
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101,465