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This paper discusses some connections between adaptive Monte Carlo algorithms and general state space Markov chains. Adaptive algorithms are iterative methods in which previously generated samples are...
We consider the problem of optimal allocation of computing budget to maximize the probability of correct selection in the ordinal optimization setting. This problem has been studied in the literature ...
This note examines the influence of covariance inflation on the distance between the measured observation and the simulated (or predicted) observation with respect to the state estimate. In order for ...
The goal of machine learning is to provide solutions which are trained by data or by experience coming from the environment. Many training algorithms exist and some brilliant successes were achieved...
Spectral clustering is a broad class of clustering procedures in which an intractable combinatorial optimization formulation of clustering is "relaxed" into a tractable eigenvector problem, and in whi...
Spectral clustering is a broad class of clustering procedures in which an intractable combinatorial optimization formulation of clustering is "relaxed" into a tractable eigenvector problem, and in whi...
Hidden Markov models (HMMs) and related models have become stan- dard in statistics during the last 15C2 years, with applications in diverse areas like speech and other statistical signal processing...
Bivariate Ordinal Data in the Perspective of the Wisconsin Epidemiologic Study of Diabetic Retinopathy: Different Statistical Directions。
Linear statistical analysis, and the least squares method specifically, achieved their modern complete form in the language of linear algebra, that is in the language of geometry. In this article we...
We consider the problem of detecting edges in piecewise smooth functions from their N-degree spectral content, which is assumed to be corrupted by noise. There are three scales involved: the “smoot...

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