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Using martingale methods, we provide bounds for the entropy of a probability measure on $\mathbb {R}^d$ with the right-hand side given in a certain integral form. As a corollary, in the one-dimensional case, we obtain a weighted log-Sobolev…

Probability · Mathematics 2015-03-19 Alexei Kulik , Taras Tymoshkevych

We propose \textit{DeepMartingale}, a deep-learning framework for the dual formulation of discrete-monitoring optimal stopping problems under continuous-time models. Leveraging a martingale representation, our method implements a…

Optimization and Control · Mathematics 2026-02-27 Junyan Ye , Hoi Ying Wong

We give a proof of the maximal inequalities of Burkholder, Davis and Gundy for real as well as Hilbert-space-valued local martingales using almost only stochastic calculus. Some parts of the exposition, especially in the infinite…

Probability · Mathematics 2013-08-13 Carlo Marinelli , Michael Röckner

Shearer's inequality bounds the sum of joint entropies of random variables in terms of the total joint entropy. We give another lower bound for the same sum in terms of the individual entropies when the variables are functions of…

Probability · Mathematics 2021-03-23 Endre Csóka , Viktor Harangi , Bálint Virág

In this paper, we propose a new approach for deriving probabilistic inequalities. Our main idea is to exploit the information of underlying distributions by virtue of the monotone likelihood ratio property and Berry-Essen inequality.…

Probability · Mathematics 2015-03-17 Xinjia Chen

The strong convergence of numerical methods for stochastic differential equations (SDEs) for $t\in[0,\infty)$ is proved. The result is applicable to any one-step numerical methods with Markov property that have the finite time strong…

Numerical Analysis · Mathematics 2023-07-12 Wei Liu , Yudong Wang

This paper derives new bounds on the difference of the entropies of two discrete random variables in terms of the local and total variation distances between their probability mass functions. The derivation of the bounds relies on maximal…

Information Theory · Computer Science 2016-11-17 Igal Sason

This paper deals with the Gaussian and bootstrap approximations to the distribution of the max statistic in high dimensions. This statistic takes the form of the maximum over components of the sum of independent random vectors and its…

Statistics Theory · Mathematics 2022-05-31 Victor Chernozhukov , Denis Chetverikov , Kengo Kato , Yuta Koike

Hoeffding has shown that tail bounds on the distribution for sampling from a finite population with replacement also apply to the corresponding cases of sampling without replacement. (A special case of this result is that binomial tail…

Probability · Mathematics 2011-07-11 Kyle J. Luh , Nicholas Pippenger

In this work, we present a generalized methodology for analyzing the convergence of quasi-optimal Taylor and Legendre approximations, applicable to a wide class of parameterized elliptic PDEs with finite-dimensional deterministic and…

Analysis of PDEs · Mathematics 2015-08-11 Hoang Tran , Clayton G. Webster , Guannan Zhang

The entropic region is formed by the collection of the Shannon entropies of all subvectors of finitely many jointly distributed discrete random variables. For four or more variables, the structure of the entropic region is mostly unknown.…

Information Theory · Computer Science 2026-03-04 E. P. Csirmaz , L. Csirmaz

We develop a martingale approach for studying continuous-time stochastic differential games of control and stopping, in a non-Markovian framework and with the control affecting only the drift term of the state-process. Under appropriate…

Probability · Mathematics 2008-08-28 Ioannis Karatzas , Ingrid-Mona Zamfirescu

We consider the stochastic integrals of multivariate point processes and study their concentration phenomena. In particular, we obtain a Bernstein type of concentration inequality through Dol\'eans-Dade exponential formula and a uniform…

Probability · Mathematics 2017-03-24 Hanchao Wang , Zhengyan Lin , Zhonggen Su

We use the martingale-theoretic approach of game-theoretic probability to incorporate imprecision into the study of randomness. In particular, we define several notions of randomness associated with interval, rather than precise,…

Probability · Mathematics 2021-06-24 Gert de Cooman , Jasper De Bock

We compute the limiting distributions of the lengths of the longest monotone subsequences of random (signed) involutions with or without conditions on the number of fixed points (and negated points) as the sizes of the involutions tend to…

Combinatorics · Mathematics 2007-05-23 Jinho Baik , Eric M. Rains

For better learning, large datasets are often split into small batches and fed sequentially to the predictive model. In this paper, we study such batch decompositions from a probabilistic perspective. We assume that data points (possibly…

Machine Learning · Computer Science 2025-04-10 Ghurumuruhan Ganesan

An estimation method is proposed for a wide variety of discrete time stochastic processes that have an intractable likelihood function but are otherwise conveniently specified by an integral transform such as the characteristic function,…

Statistics Theory · Mathematics 2009-09-29 T. Merkouris

The present paper is devoted to the second part of our project on asymmetric maximal inequalities, where we consider martingales in continuous time. Let $(\mathcal M,\tau)$ be a noncommutative probability space equipped with a continuous…

Probability · Mathematics 2016-11-07 Guixiang Hong , Marius Junge , Javier Parcet

We generalize a famous tail Doob's inequality, relative two non-negative random variables, arising in the martingale theory, in two directions: on the more general source data and on the random variables belonging to the so-called Grand…

Probability · Mathematics 2022-06-03 M. R. Formica , E. Ostrovsky , L. Sirota

In this paper, we analyze the two time-scale stochastic approximation (TTSSA) algorithm introduced in Borkar (1997) using a martingale approach. This approach leads to simple sufficient conditions for the iterations to be bounded almost…

Machine Learning · Statistics 2026-03-17 Mathukumalli Vidyasagar
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