Titlebook: Convexity and Concentration; Eric Carlen,Mokshay Madiman,Elisabeth M. Werner Conference proceedings 2017 Springer Science+Business Media L

擴(kuò)張

Interpolation of Probability Measures on Graphsdetailed proofs and constructions in the particular case of an interpolation between two finitely supported probability measures on ., with a stochastic domination assumption. We also present other types of interpolations, in particular Léonard’s entropic interpolations and discuss the relationships between these constructions.

規(guī)章

Entropy and Thinning of Discrete Random Variablestheir counterparts in the continuous case. The results we consider are information theoretic approaches to Poisson approximation, the maximum entropy property of the Poisson distribution, discrete concentration (Poincaré and logarithmic Sobolev) inequalities, monotonicity of entropy and concavity of entropy in the Shepp–Olkin regime.

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絕緣

Log-Concave Functionsog-concave functions; duality; inequalities including the Prékopa-Leindler inequality and the functional form of Blaschke-Santaló inequality and its converse; functional versions of area measure and mixed volumes; valuations on log-concave functions.

Obstreperous

Does Election Matter in a Hybrid Regime?,detailed proofs and constructions in the particular case of an interpolation between two finitely supported probability measures on ., with a stochastic domination assumption. We also present other types of interpolations, in particular Léonard’s entropic interpolations and discuss the relationships

領(lǐng)導(dǎo)權(quán)

Does Election Matter in a Hybrid Regime?,their counterparts in the continuous case. The results we consider are information theoretic approaches to Poisson approximation, the maximum entropy property of the Poisson distribution, discrete concentration (Poincaré and logarithmic Sobolev) inequalities, monotonicity of entropy and concavity of

Externalize

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IRATE

Janneke van Mens-Verhulst,Greta Noordenbosn for many models, and there has been significant recent progress in obtaining more quantitative, non-asymptotic results. In this paper, we describe a systematic approach to bounding rates of convergence and proving tail inequalities for the empirical spectral measures of a wide variety of random ma

averse

D. R. Beaman,R. Lewis,J. A. Isasio any of its individual arguments will tend to take values very close to its expectation. This phenomenon is most completely understood when the arguments are mutually independent random variables, and there exist several powerful complementary methods for proving concentration inequalities, such as