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15,709
Convergence rates of posterior distributions
 Ann. Statist
, 2000
"... We consider the asymptotic behavior of posterior distributions and Bayes estimators for infinitedimensional statistical models. We give general results on the rate of convergence of the posterior measure. These are applied to several examples, including priors on finite sieves, logspline models, D ..."
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Cited by 110 (19 self)
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We consider the asymptotic behavior of posterior distributions and Bayes estimators for infinitedimensional statistical models. We give general results on the rate of convergence of the posterior measure. These are applied to several examples, including priors on finite sieves, logspline models
On the convergence rates of genetic algorithms
 Theoretical Computer Science
, 1999
"... Bounds on the convergent rate is an important problem in the foundations of genetic algorithm. This paper obtained some bounds on the convergent rate of genetic algorithms by Markov chain theory. The main result is that the algorithms convergence in geometric rate under the meaning of probability me ..."
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Cited by 14 (5 self)
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Bounds on the convergent rate is an important problem in the foundations of genetic algorithm. This paper obtained some bounds on the convergent rate of genetic algorithms by Markov chain theory. The main result is that the algorithms convergence in geometric rate under the meaning of probability
On the Componentwise Convergence Rate
"... In this paper we investigate the convergence rate of a sequence of vectors provided that the convergence rates of the components are known. The result of this investigation is then used to study the mstep convergence rate of sequences. Key Words. convergence rate  Qfactor  multistep convergence ..."
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In this paper we investigate the convergence rate of a sequence of vectors provided that the convergence rates of the components are known. The result of this investigation is then used to study the mstep convergence rate of sequences. Key Words. convergence rate  Qfactor  multi
Convergence Rate for Consensus with Delays
, 2007
"... We study the problem of reaching a consensus in the values of a distributed system of agents with timevarying connectivity in the presence of delays. We consider a widely studied consensus algorithm, in which at each time step, every agent forms a weighted average of its own value with values recei ..."
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Cited by 25 (2 self)
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received from the neighboring agents. We study an asynchronous operation of this algorithm using delayed agent values. Our focus is on establishing convergence rate results for this algorithm. In particular, we first show convergence to consensus under a bounded delay condition and some connectivity
Convergence rates for regularization with . . .
, 2010
"... Tikhonov regularization with ppowers of the weighted ℓp norms as penalties, with p ∈ (1, 2), have been employed recently in reconstruction of sparse solutions of illposed inverse problems. This paper shows convergence rates for such a regularization with respect to the norm of the weighted space ..."
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Tikhonov regularization with ppowers of the weighted ℓp norms as penalties, with p ∈ (1, 2), have been employed recently in reconstruction of sparse solutions of illposed inverse problems. This paper shows convergence rates for such a regularization with respect to the norm of the weighted
CONVERGENCE RATES OF ORTHOGONAL SERIES
"... Abstract. General conditions for convergence rates of nonparametric orthogonal series estimators of the regression function f(x) = E(Y X = x) are considered. The estimators are obtained by the least squares method on the basis of a random observation sample (Yi,Xi), i = 1,..., n, where Xi ∈ A ⊂ R ..."
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Abstract. General conditions for convergence rates of nonparametric orthogonal series estimators of the regression function f(x) = E(Y X = x) are considered. The estimators are obtained by the least squares method on the basis of a random observation sample (Yi,Xi), i = 1,..., n, where Xi ∈ A
Convergence Rate of Incremental Subgradient Algorithms
 Stochastic Optimization: Algorithms and Applications
, 2000
"... We consider a class of subgradient methods for minimizing a convex function that consists of the sum of a large number of component functions. This type of minimization arises in a dual context from Lagrangian relaxation of the coupling constraints of large scale separable problems. The idea is to p ..."
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Cited by 65 (6 self)
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squares problems, such as those arising in the training of neural networks, and it has resulted in a much better practical rate of convergence than the steepest descent method. In this paper, we present convergence results and estimates of the convergence rate of a number of variants of incremental
On the convergence rate of the unscented transformation
, 2011
"... Nonlinear statespace models driven by differential equations have been widely used in science. Their statistical inference generally requires computing the mean and covariance matrix of some nonlinear function of the state variables, which can be done in several ways. For example, such computations ..."
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Cited by 1 (1 self)
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by Julier and Uhlmann (1997) to overcome these difficulties, but it lacks of theoretical justification. In this paper, we derive some theoretical properties of the unscented transformation and contrast it with the method of linear approximation. Particularly, we derive the convergence rate of the unscented
Results 1  10
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15,709