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water waves with an arbitrary distribution
"... dynamics methods for solitary gravitycapillary ..."
Multichannel blind separation and deconvolution of sources with arbitrary distributions
 In Proc. IEEE Workshop on Neural Networks for Signal Processing, pages 436445, Almelia Island
, 1997
"... Abstract { Blind deconvolution and separation of linearly mixed and convolved sources is an important andchallenging task for numerous applications. While several recentlydeveloped algorithms have shown promise in these tasks, these techniques may fail to separate signal mixtures containing both su ..."
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Cited by 37 (15 self)
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using a rigorouslyderived su cient criterion for stability and then selects the appropriate nonlinearityforeachchannel suchthat local convergence conditions of the algorithm are satis ed. Extensive simulations show thevalidity and e ciency of our method to blindly extract mixtures of arbitrarydistributed
Transfer of Photometric Quantities at Arbitrary Distribution Temperatures
"... Characteristics of a photometric setup used for the measurement of photometric quantities at the National Institute of Metrology (UME) in Turkey are reported. A high accuracy method is developed to adjust the laments of incandescent lamps with respect to the optical axis. The method also provides th ..."
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the basis for low uncertainty distance measurements between lamp laments and photometers. Transfer of photometric quantities at arbitrary distribution temperatures is examined both theoretically and experimentally, and the results are compared to the calibration values of the Bureau International Des Poids
Pricing of Options and Real Options For Arbitrary Distributions
, 1999
"... this paper we use the risk neutral technique discussed by Luenberger [1997] to develop a technique that can be used to price derivatives and real options involving arbitrary distributions. In Section 1 we begin with a quick summary of Luenberger's discussion. In Section 2 we implement the techn ..."
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Cited by 1 (0 self)
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this paper we use the risk neutral technique discussed by Luenberger [1997] to develop a technique that can be used to price derivatives and real options involving arbitrary distributions. In Section 1 we begin with a quick summary of Luenberger's discussion. In Section 2 we implement
Completely Derandomized SelfAdaptation in Evolution Strategies
 Evolutionary Computation
, 2001
"... This paper puts forward two useful methods for selfadaptation of the mutation distribution  the concepts of derandomization and cumulation. Principle shortcomings of the concept of mutative strategy parameter control and two levels of derandomization are reviewed. Basic demands on the selfadapta ..."
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Cited by 549 (58 self)
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adaptation of arbitrary (normal) mutation distributions are developed. Applying arbitrary, normal mutation distributions is equivalent to applying a general, linear problem encoding.
Boosting and differential privacy
, 2010
"... Boosting is a general method for improving the accuracy of learning algorithms. We use boosting to construct improved privacypreserving synopses of an input database. These are data structures that yield, for a given set Q of queries over an input database, reasonably accurate estimates of the resp ..."
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Cited by 648 (14 self)
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of the responses to every query in Q, even when the number of queries is much larger than the number of rows in the database. Given a base synopsis generator that takes a distribution on Q and produces a “weak ” synopsis that yields “good ” answers for a majority of the weight in Q, our Boosting for Queries
High dimensional graphs and variable selection with the Lasso
 ANNALS OF STATISTICS
, 2006
"... The pattern of zero entries in the inverse covariance matrix of a multivariate normal distribution corresponds to conditional independence restrictions between variables. Covariance selection aims at estimating those structural zeros from data. We show that neighborhood selection with the Lasso is a ..."
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Cited by 736 (22 self)
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The pattern of zero entries in the inverse covariance matrix of a multivariate normal distribution corresponds to conditional independence restrictions between variables. Covariance selection aims at estimating those structural zeros from data. We show that neighborhood selection with the Lasso
Maximum entropy markov models for information extraction and segmentation
, 2000
"... Hidden Markov models (HMMs) are a powerful probabilistic tool for modeling sequential data, and have been applied with success to many textrelated tasks, such as partofspeech tagging, text segmentation and information extraction. In these cases, the observations are usually modeled as multinomial ..."
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Cited by 561 (18 self)
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as multinomial distributions over a discrete vocabulary, and the HMM parameters are set to maximize the likelihood of the observations. This paper presents a new Markovian sequence model, closely related to HMMs, that allows observations to be represented as arbitrary overlapping features (such as word
Randomized Gossip Algorithms
 IEEE TRANSACTIONS ON INFORMATION THEORY
, 2006
"... Motivated by applications to sensor, peertopeer, and ad hoc networks, we study distributed algorithms, also known as gossip algorithms, for exchanging information and for computing in an arbitrarily connected network of nodes. The topology of such networks changes continuously as new nodes join a ..."
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Cited by 532 (5 self)
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distribute the computational burden and in which a node communicates with a randomly chosen neighbor. We analyze the averaging problem under the gossip constraint for an arbitrary network graph, and find that the averaging time of a gossip algorithm depends on the second largest eigenvalue of a doubly
Linear models and empirical bayes methods for assessing differential expression in microarray experiments.
 Stat. Appl. Genet. Mol. Biol.
, 2004
"... Abstract The problem of identifying differentially expressed genes in designed microarray experiments is considered. Lonnstedt and Speed (2002) derived an expression for the posterior odds of differential expression in a replicated twocolor experiment using a simple hierarchical parametric model. ..."
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Cited by 1321 (24 self)
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. The purpose of this paper is to develop the hierarchical model of Lonnstedt and Speed (2002) into a practical approach for general microarray experiments with arbitrary numbers of treatments and RNA samples. The model is reset in the context of general linear models with arbitrary coefficients and contrasts
Results 1  10
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