scholarly journals Conditional Expectation, Entropy, and Transport for Convex Gibbs Laws in Free Probability

Author(s):  
David Jekel

Abstract Let $(X_1,\dots ,X_m)$ be self-adjoint noncommutative random variables distributed according to the free Gibbs law given by a sufficiently regular convex and semi-concave potential $V$, and let $(S_1,\dots ,S_m)$ be a free semicircular family. For $k < m$, we show that conditional expectations and conditional non-microstates free entropy given $X_1$, …, $X_k$ arise as the large $N$ limit of the corresponding conditional expectations and entropy for the $N \times N$ random matrix models associated to $V$. Then, by studying conditional transport of measure for the matrix models, we construct an isomorphism $\mathrm{W}^*(X_1,\dots ,X_m) \to \mathrm{W}^*(S_1,\dots ,S_m)$ that maps $\mathrm{W}^*(X_1,\dots ,X_k)$ to $\mathrm{W}^*(S_1,\dots ,S_k)$ for each $k = 1, \dots , m$ and that also witnesses the Talagrand inequality for the law of $(X_1,\dots ,X_m)$ relative to the law of $(S_1,\dots ,S_m)$.

2012 ◽  
Vol 01 (02) ◽  
pp. 1150008 ◽  
Author(s):  
ROLAND SPEICHER ◽  
CARLOS VARGAS

Motivated by the asymptotic collective behavior of random and deterministic matrices, we propose an approximation (called "free deterministic equivalent") to quite general random matrix models, by replacing the matrices with operators satisfying certain freeness relations. We comment on the relation between our free deterministic equivalent and deterministic equivalents considered in the engineering literature. We do not only consider the case of square matrices, but also show how rectangular matrices can be treated. Furthermore, we emphasize how operator-valued free probability techniques can be used to solve our free deterministic equivalents. As an illustration of our methods we show how the free deterministic equivalent of a random matrix model from [6] can be treated and we thus recover in a conceptual way the results from [6]. On a technical level, we generalize a result from scalar-valued free probability, by showing that randomly rotated deterministic matrices of different sizes are asymptotically free from deterministic rectangular matrices, with amalgamation over a certain algebra of projections. In Appendix A, we show how estimates for differences between Cauchy transforms can be extended from a neighborhood of infinity to a region close to the real axis. This is of some relevance if one wants to compare the original random matrix problem with its free deterministic equivalent.


Author(s):  
Tomohiro Hayase

We investigate parameter identifiability of spectral distributions of random matrices. In particular, we treat compound Wishart type and signal-plus-noise type. We show that each model is identifiable up to some kind of rotation of parameter space. Our method is based on free probability theory.


2016 ◽  
Vol 05 (02) ◽  
pp. 1650005
Author(s):  
Jian Xu ◽  
Engui Fan ◽  
Yang Chen

In this paper, we analyze the large N-limit for random matrix with external source with three distinct eigenvalues. And we confine ourselves in the Hermite case and the three distinct eigenvalues are [Formula: see text]. For the case [Formula: see text], we establish the universal behavior of local eigenvalue correlations in the limit [Formula: see text], which is known from unitarily invariant random matrix models. Thus, local eigenvalue correlations are expressed in terms of the sine kernel in the bulk and in terms of the Airy kernel at the edge of the spectrum. The result can be obtained by analyzing [Formula: see text] Riemann–Hilbert problem via nonlinear steepest decent method.


2012 ◽  
Vol 12 (4) ◽  
pp. 567-572 ◽  
Author(s):  
Ivailo I. Dimov ◽  
Petter N. Kolm ◽  
Lee Maclin ◽  
Dan Y. C. Shiber

2011 ◽  
Vol 74 (10) ◽  
pp. 102001 ◽  
Author(s):  
B Vanderheyden ◽  
A D Jackson

2020 ◽  
Vol 955 ◽  
pp. 115051
Author(s):  
M.A. Nowak ◽  
W. Tarnowski

1997 ◽  
Vol 55 (4) ◽  
pp. 4100-4106 ◽  
Author(s):  
Romuald A. Janik ◽  
Maciej A. Nowak ◽  
Gábor Papp ◽  
Jochen Wambach ◽  
Ismail Zahed

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