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606,688
Surrogate Modeling Based on Statistical Techniques for Multifidelity Optimization
, 2014
"... Designing and optimizing complex systems generally requires the use of numerical models. However, it is often too expensive to evaluate these models at each step of an optimization problem. Instead surrogate models can be used to explore the design space, as they are much cheaper to evaluate. Constr ..."
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. Constructing a surrogate becomes challenging when different numerical models are used to compute the same quantity, but with different levels of fidelity (i.e., different levels of uncertainty in the models). In this work, we propose a method based on statistical techniques to build such a multifidelity
Multifidelity optimization via surrogate modelling
 Royal Society
, 2007
"... This paper demonstrates the application of correlated Gaussian process based approximations to optimization where multiple levels of analysis are available, using an extension to the geostatistical method of cokriging. An exchange algorithm is used to choose which points of the search space to samp ..."
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Cited by 31 (3 self)
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to sample within each level of analysis. The derivation of the cokriging equations is presented in an intuitive manner, along with a new variance estimator to account for varying degrees of computational ‘noise ’ in the multiple levels of analysis. A multifidelity wing optimization is used to demonstrate
Option Pricing: A Simplified Approach
 Journal of Financial Economics
, 1979
"... This paper presents a simple discretetime model for valumg optlons. The fundamental econonuc principles of option pricing by arbitrage methods are particularly clear In this setting. Its development requires only elementary mathematics, yet it contains as a special limiting case the celebrated Blac ..."
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Cited by 1016 (10 self)
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This paper presents a simple discretetime model for valumg optlons. The fundamental econonuc principles of option pricing by arbitrage methods are particularly clear In this setting. Its development requires only elementary mathematics, yet it contains as a special limiting case the celebrated
The Ant System: Optimization by a colony of cooperating agents
 IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICSPART B
, 1996
"... An analogy with the way ant colonies function has suggested the definition of a new computational paradigm, which we call Ant System. We propose it as a viable new approach to stochastic combinatorial optimization. The main characteristics of this model are positive feedback, distributed computation ..."
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Cited by 1300 (46 self)
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An analogy with the way ant colonies function has suggested the definition of a new computational paradigm, which we call Ant System. We propose it as a viable new approach to stochastic combinatorial optimization. The main characteristics of this model are positive feedback, distributed
Valuing American options by simulation: A simple leastsquares approach
 Review of Financial Studies
, 2001
"... This article presents a simple yet powerful new approach for approximating the value of America11 options by simulation. The kcy to this approach is the use of least squares to estimate the conditional expected payoff to the optionholder from continuation. This makes this approach readily applicable ..."
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Cited by 517 (9 self)
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factor string model of the term structure. One of the most important problems in option pricing theory is the valuation and optimal exercise of derivatives with Americanstyle exercise features. These types of derivatives are found in all major financial markets including the equity, commodity, foreign
Markov Random Field Models in Computer Vision
, 1994
"... . A variety of computer vision problems can be optimally posed as Bayesian labeling in which the solution of a problem is defined as the maximum a posteriori (MAP) probability estimate of the true labeling. The posterior probability is usually derived from a prior model and a likelihood model. The l ..."
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Cited by 516 (18 self)
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. A variety of computer vision problems can be optimally posed as Bayesian labeling in which the solution of a problem is defined as the maximum a posteriori (MAP) probability estimate of the true labeling. The posterior probability is usually derived from a prior model and a likelihood model
Object Detection with Discriminatively Trained Part Based Models
"... We describe an object detection system based on mixtures of multiscale deformable part models. Our system is able to represent highly variable object classes and achieves stateoftheart results in the PASCAL object detection challenges. While deformable part models have become quite popular, their ..."
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Cited by 1422 (49 self)
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We describe an object detection system based on mixtures of multiscale deformable part models. Our system is able to represent highly variable object classes and achieves stateoftheart results in the PASCAL object detection challenges. While deformable part models have become quite popular
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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all be understood in terms of exact or approximate forms of these variational representations. The variational approach provides a complementary alternative to Markov chain Monte Carlo as a general source of approximation methods for inference in largescale statistical models.
MultiFidelity Aerodynamic Optimization Using Treed MetaModels
"... The multifidelity Treed MetaModel (TMM) framework developed and applied here creates a treebased partitioning of an aerodynamic design space and employs independent Kriging surfaces in each partition to globally model computationally inexpensive lowfidelity analysis data. Using the lowfidelity ..."
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metamodel to select points for evaluation with a highfidelity analysis tool, a multifidelity global model is created from collocated high and low fidelity data points. A steadystate genetic algorithm is used to construct an optimal partitioning scheme based on the number of points in each partition
Minimum Error Rate Training in Statistical Machine Translation
, 2003
"... Often, the training procedure for statistical machine translation models is based on maximum likelihood or related criteria. A general problem of this approach is that there is only a loose relation to the final translation quality on unseen text. In this paper, we analyze various training cri ..."
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Cited by 757 (7 self)
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Often, the training procedure for statistical machine translation models is based on maximum likelihood or related criteria. A general problem of this approach is that there is only a loose relation to the final translation quality on unseen text. In this paper, we analyze various training
Results 1  10
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606,688