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302,980
Sequential data assimilation with a nonlinear quasigeostrophic model using Monte Carlo methods to forecast error statistics
 J. Geophys. Res
, 1994
"... . A new sequential data assimilation method is discussed. It is based on forecasting the error statistics using Monte Carlo methods, a better alternative than solving the traditional and computationally extremely demanding approximate error covariance equation used in the extended Kalman filter. The ..."
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Cited by 782 (22 self)
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. A new sequential data assimilation method is discussed. It is based on forecasting the error statistics using Monte Carlo methods, a better alternative than solving the traditional and computationally extremely demanding approximate error covariance equation used in the extended Kalman filter
Sequential quasiMonte Carlo
, 2014
"... We develop a new class of algorithms, SQMC (Sequential QuasiMonte Carlo), as a variant of SMC (Sequential Monte Carlo) based on lowdiscrepancy point sets. The complexity of SQMC is O(N logN), where N is the number of simulations at each iteration, and its error rate is smaller than the Monte Carlo ..."
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Cited by 3 (2 self)
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We develop a new class of algorithms, SQMC (Sequential QuasiMonte Carlo), as a variant of SMC (Sequential Monte Carlo) based on lowdiscrepancy point sets. The complexity of SQMC is O(N logN), where N is the number of simulations at each iteration, and its error rate is smaller than the Monte
Robust Monte Carlo Localization for Mobile Robots
, 2001
"... Mobile robot localization is the problem of determining a robot's pose from sensor data. This article presents a family of probabilistic localization algorithms known as Monte Carlo Localization (MCL). MCL algorithms represent a robot's belief by a set of weighted hypotheses (samples), whi ..."
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Cited by 826 (88 self)
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Mobile robot localization is the problem of determining a robot's pose from sensor data. This article presents a family of probabilistic localization algorithms known as Monte Carlo Localization (MCL). MCL algorithms represent a robot's belief by a set of weighted hypotheses (samples
On Sequential Monte Carlo Sampling Methods for Bayesian Filtering
 STATISTICS AND COMPUTING
, 2000
"... In this article, we present an overview of methods for sequential simulation from posterior distributions. These methods are of particular interest in Bayesian filtering for discrete time dynamic models that are typically nonlinear and nonGaussian. A general importance sampling framework is develop ..."
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Cited by 1032 (76 self)
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In this article, we present an overview of methods for sequential simulation from posterior distributions. These methods are of particular interest in Bayesian filtering for discrete time dynamic models that are typically nonlinear and nonGaussian. A general importance sampling framework is developed that unifies many of the methods which have been proposed over the last few decades in several different scientific disciplines. Novel extensions to the existing methods are also proposed. We show in particular how to incorporate local linearisation methods similar to those which have previously been employed in the deterministic filtering literature; these lead to very effective importance distributions. Furthermore we describe a method which uses RaoBlackwellisation in order to take advantage of the analytic structure present in some important classes of statespace models. In a final section we develop algorithms for prediction, smoothing and evaluation of the likelihood in dynamic models.
Implementing QuasiMonte Carlo Simulations with Linear Transformations
 Computational Management Science
, 2008
"... Pricing exotic multiasset pathdependent options requires extensive Monte Carlo simulations. In the recent years the interest to the Quasimonte Carlo technique has been renewed and several results have been proposed in order to improve its efficiency with the notion of effective dimension. To this ..."
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Cited by 3 (2 self)
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Pricing exotic multiasset pathdependent options requires extensive Monte Carlo simulations. In the recent years the interest to the Quasimonte Carlo technique has been renewed and several results have been proposed in order to improve its efficiency with the notion of effective dimension
Near Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
, 2004
"... Suppose we are given a vector f in RN. How many linear measurements do we need to make about f to be able to recover f to within precision ɛ in the Euclidean (ℓ2) metric? Or more exactly, suppose we are interested in a class F of such objects— discrete digital signals, images, etc; how many linear m ..."
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Cited by 1513 (20 self)
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Suppose we are given a vector f in RN. How many linear measurements do we need to make about f to be able to recover f to within precision ɛ in the Euclidean (ℓ2) metric? Or more exactly, suppose we are interested in a class F of such objects— discrete digital signals, images, etc; how many linear measurements do we need to recover objects from this class to within accuracy ɛ? This paper shows that if the objects of interest are sparse or compressible in the sense that the reordered entries of a signal f ∈ F decay like a powerlaw (or if the coefficient sequence of f in a fixed basis decays like a powerlaw), then it is possible to reconstruct f to within very high accuracy from a small number of random measurements. typical result is as follows: we rearrange the entries of f (or its coefficients in a fixed basis) in decreasing order of magnitude f  (1) ≥ f  (2) ≥... ≥ f  (N), and define the weakℓp ball as the class F of those elements whose entries obey the power decay law f  (n) ≤ C · n −1/p. We take measurements 〈f, Xk〉, k = 1,..., K, where the Xk are Ndimensional Gaussian
QuasiMonte Carlo and Monte Carlo Methods and their Application in Finance
"... We give an introduction to and a survey on the use of QuasiMonte Carlo and of Monte Carlo methods especially in option pricing and in risk management. We concentrate on new techniques from the QuasiMonte Carlo theory. 1 ..."
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Cited by 2 (2 self)
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We give an introduction to and a survey on the use of QuasiMonte Carlo and of Monte Carlo methods especially in option pricing and in risk management. We concentrate on new techniques from the QuasiMonte Carlo theory. 1
QuasiMonte Carlo approximations in stochastic optimization
"... • Computational methods for solving stochastic programs require (first) a discretization of the underlying probability distribution induced by a numerical integration scheme and (second) an efficient solver for the finitedimensional program. ..."
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• Computational methods for solving stochastic programs require (first) a discretization of the underlying probability distribution induced by a numerical integration scheme and (second) an efficient solver for the finitedimensional program.
Results 1  10
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302,980