Details
Random Matrices and Iterated Random Functions
Münster, October 2011Springer Proceedings in Mathematics & Statistics, Band 53
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96,29 € |
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| Verlag: | Springer |
| Format: | |
| Veröffentl.: | 28.08.2013 |
| ISBN/EAN: | 9783642388064 |
| Sprache: | englisch |
| Anzahl Seiten: | 265 |
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Beschreibungen
Random Matrices are one of the major research areas in modern probability theory, due to their prominence in many different fields such as nuclear physics, statistics, telecommunication, free probability, non-commutative geometry, and dynamical systems. A great deal of recent work has focused on the study of spectra of large random matrices on the one hand and on iterated random functions, especially random difference equations, on the other. However, the methods applied in these two research areas are fairly dissimilar. Motivated by the idea that tools from one area could potentially also be helpful in the other, the volume editors have selected contributions that present results and methods from random matrix theory as well as from the theory of iterated random functions. This work resulted from a workshop that was held in Münster, Germany in 2011. The aim of the workshop was to bring together researchers from two fields of probability theory: random matrix theory and the theory of iterated random functions. Random matrices play fundamental, yet very different roles in the two fields. Accordingly, leading figures and young researchers gave talks on their field of interest that were also accessible to a broad audience.
E. Le Page: Tails of a stationary probability measure for
an affine stochastic recursion on the line.- Yv. Guivarc’h: On homogeneity at infinity of stationary measures for
affine stochastic recursions.- M. Stolz:
Limit theorems for random elements of the compact classical groups.- T. Kriecherbauer: Universality of local
eigenvalue statistics.- R. Speicher:
Asymptotic eigenvalue distribution of random matrices and free stochastic
analysis.- M. Peigné: Conditioned
random walk in Weyl chambers and renewal theory in a cone.- D. Buraczewski: The linear stochastic
equation R =_d \sum_{ i=1}^N A_iR_i + B in the critical case.- J. Collamore: Tail estimates for
stochastic fixed point equations.- S. Mentemeier:
On multivariate random difference equations.- M. Olvera-Cravioto: Tail asymptotics for solutions of stochastic
fixed point equations on trees.- E. Damek:
On fixed points of generalized multidimensional affine recursions.- G. Alsmeyer: The functional equation of
the smoothing transform.– O. Friesen, M.
Löwe: Limit theorems for the eigenvalues of random matrices with weakly
correlated entries.
E. Le Page: Tails of a stationary probability measure for
an affine stochastic recursion on the line.- Yv. Guivarc’h: On homogeneity at infinity of stationary measures for
affine stochastic recursions.- M. Stolz:
Limit theorems for random elements of the compact classical groups.- T. Kriecherbauer: Universality of local
eigenvalue statistics.- R. Speicher:
Asymptotic eigenvalue distribution of random matrices and free stochastic
analysis.- M. Peigné: Conditioned
random walk in Weyl chambers and renewal theory in a cone.- D. Buraczewski: The linear stochastic
equation R =_d \sum_{ i=1}^N A_iR_i + B in the critical case.- J. Collamore: Tail estimates for
stochastic fixed point equations.- S. Mentemeier:
On multivariate random difference equations.- M. Olvera-Cravioto: Tail asymptotics for solutions of stochastic
fixed point equations on trees.- E. Damek:
On fixed points of generalized multidimensional affine recursions.- G. Alsmeyer: The functional equation of
the smoothing transform.– O. Friesen, M.
Löwe: Limit theorems for the eigenvalues of random matrices with weakly
correlated entries.
Random Matrices are one of the major research areas in modern probability theory, due to their prominence in many different fields such as nuclear physics, statistics, telecommunication, free probability, non-commutative geometry, and dynamical systems. A great deal of recent work has focused on the study of spectra of large random matrices on the one hand and on iterated random functions, especially random difference equations, on the other. However, the methods applied in these two research areas are fairly dissimilar. Motivated by the idea that tools from one area could potentially also be helpful in the other, the volume editors have selected contributions that present results and methods from random matrix theory as well as from the theory of iterated random functions. This work resulted from a workshop that was held in Münster, Germany in 2011. The aim of the workshop was to bring together researchers from two fields of probability theory: random matrix theory and the theory of iterated random functions. Random matrices play fundamental, yet very different roles in the two fields. Accordingly, leading figures and young researchers gave talks on their field of interest that were also accessible to a broad audience.
Brings together two active areas of modern probability theory Provides survey articles that are also readable for non-experts Refereed according to journal standards Includes supplementary material: sn.pub/extras
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