A. A. Markov, ses probabilités en chaîne et les statistiques linguistiques
Page 1 Next
M. Petruszewycz (1979)
Mathématiques et Sciences Humaines
Gary J. Koehler (1980)
RAIRO - Operations Research - Recherche Opérationnelle
Guoqiang Huan, Ying Li, Zhanjie Song (2016)
Applications of Mathematics
The research on the robust principal component analysis has been attracting much attention recently. Generally, the model assumes sparse noise and characterizes the error term by the -norm. However, the sparse noise has clustering effect in practice so using a certain -norm simply is not appropriate for modeling. In this paper, we propose a novel method based on sparse Bayesian learning principles and Markov random fields. The method is proved to be very effective for low-rank matrix recovery...
Yi Lu Shuanming Li, Garrido, José (2009)
RACSAM
Mitzenmacher, M., Oliveira, R., Spencer, J. (2004)
The Electronic Journal of Combinatorics [electronic only]
Brianzoni, Serena, Mammana, Cristiana, Michetti, Elisabetta, Zirilli, Francesco (2008)
Discrete Dynamics in Nature and Society
Bartek Wilczyński (2008)
Banach Center Publications
In this paper we present the extension of the kinetic logic proposed by René Thomas for analysis of genetic regulatory gene networks. We consider the case with a Gaussian noise added to the regulation function and propose a method of analyzing the resulting model with a discrete time Markov model.
Pemantle, Robin (2007)
Probability Surveys [electronic only]
Fritzsche, David, Mehrmann, Volker, Szyld, Daniel B., Virnik, Elena (2007)
ETNA. Electronic Transactions on Numerical Analysis [electronic only]
Minh, Do L., Bhaskar, R. (2006)
Journal of Applied Mathematics and Decision Sciences
Karl Hinderer, Harro Walk (1972)
Mathematische Zeitschrift
Antoni Donigiewicz (2004)
Control and Cybernetics
L. Sanz, J. A. Alonso (2010)
Mathematical Modelling of Natural Phenomena
Approximate aggregation techniques consist of introducing certain approximations that allow one to reduce a complex system involving many coupled variables obtaining a simpler ʽʽaggregated systemʼʼ governed by a few variables. Moreover, they give results that allow one to extract information about the complex original system in terms of the behavior of the reduced one. Often, the feature that allows one to carry out such a reduction is the presence...
Wolfgang Stadje (1990)
Annales de l'I.H.P. Probabilités et statistiques
Ismail, Mourad E.H., Letessier, Jean, Valent, Galliano (1992)
International Journal of Mathematics and Mathematical Sciences
Kuo, C.-T., Lim, J.-T., Meerkov, S.M. (1996)
Mathematical Problems in Engineering
Rajmund Drenyovszki, Lóránt Kovács, Kálmán Tornai, András Oláh, István Pintér (2017)
Kybernetika
In our paper we investigate the applicability of independent and identically distributed random sequences, first order Markov and higher order Markov chains as well as semi-Markov processes for bottom-up electricity load modeling. We use appliance time series from publicly available data sets containing fine grained power measurements. The comparison of models are based on metrics which are supposed to be important in power systems like Load Factor, Loss of Load Probability. Furthermore, we characterize...
Antonio López Quílez, Enriqueta Vercher (1992)
Trabajos de Investigación Operativa
En este trabajo se estudia la eficiencia de un conjunto de algoritmos, exactos e iterativos, para el problema de obtener la distribución estacionaria de una cadena de Markov homogénea, irreducible y finita. Se presentan los resultados computacionales obtenidos al resolver problemas de diferentes tipos y tamaños, aleatoriamente generados, así como el tratamiento estadístico realizado sobre los mismos. Se ha comparado la estabilidad de estos algoritmos frente a la pérdida de irreducibilidad y la existencia...
Sophie Schbath (1997)
ESAIM: Probability and Statistics
Sophie Schbath (2010)
ESAIM: Probability and Statistics
Identifying words with unexpected frequencies is an important problem in the analysis of long DNA sequences. To solve it, we need an approximation of the distribution of the number of occurrences N(W) of a word W. Modeling DNA sequences with m-order Markov chains, we use the Chen-Stein method to obtain Poisson approximations for two different counts. We approximate the “declumped” count of W by a Poisson variable and the number of occurrences N(W) by a compound Poisson variable. Combinatorial...
Page 1 Next