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We consider finite blocklength lossy compression of information sources whose components are independent but non-identically distributed. Crucially, Gaussian sources with memory and quadratic distortion can be cast in this form. We show…

Information Theory · Computer Science 2026-02-11 Eyyup Tasci , Victoria Kostina

In this paper a new generalization of the hyper-Poisson distribution is proposed using the Mittag-Leffler function. The hyper-Poisson, displaced Poisson, Poisson and geometric distributions among others are seen as particular cases. This…

Statistics Theory · Mathematics 2014-11-05 Subrata Chakraborty , S. H. Ong

The problem of maximizing non-negative submodular functions has been studied extensively in the last few years. However, most papers consider submodular set functions. Recently, several advances have been made for the more general case of…

Discrete Mathematics · Computer Science 2016-11-29 Corinna Gottschalk , Britta Peis

Stationary ergodic processes with finite alphabets are estimated by finite memory processes from a sample, an n-length realization of the process, where the memory depth of the estimator process is also estimated from the sample using…

Statistics Theory · Mathematics 2013-07-25 Zsolt Talata

Suppose that there are n bins, and balls arrive in a Poisson process at rate \lambda n, where \lambda >0 is a constant. Upon arrival, each ball chooses a fixed number d of random bins, and is placed into one with least load. Balls have…

Probability · Mathematics 2007-05-23 Malwina J. Luczak , Colin McDiarmid

A famous characterization theorem due to C.F. Gauss states that the maximum likelihood estimator (MLE) of the parameter in a location family is the sample mean for all samples of all sample sizes if and only if the family is Gaussian. There…

Statistics Theory · Mathematics 2014-03-13 Mitia Duerinckx , Christophe Ley , Yvik Swan

Standard regularized training procedures correspond to maximizing a posterior distribution over parameters, known as maximum a posteriori (MAP) estimation. However, model parameters are of interest only insomuch as they combine with the…

Machine Learning · Computer Science 2023-11-28 Shikai Qiu , Tim G. J. Rudner , Sanyam Kapoor , Andrew Gordon Wilson

Let $X_1, \ldots, X_n$ be independent random points drawn from an absolutely continuous probability measure with density $f$ in $\mathbb{R}^d$. Under mild conditions on $f$, we derive a Poisson limit theorem for the number of large…

Probability · Mathematics 2018-11-20 László Györfi , Norbert Henze , Harro Walk

We study the best approximation problem: \[ \displaystyle \min_{\alpha\in \mathbb R^m}\max_{1\leq i\leq n}\left|y_i -\sum_{j=1}^m \alpha_j \Gamma_j ({\bf x}_i) \right|. \] Here: $\Gamma:=\left\{\Gamma_1,...,\Gamma_m\right\}$ is a list of…

Optimization and Control · Mathematics 2022-09-16 Steven B. Damelin , Michael Werman

We introduce missingness-MDPs (miss-MDPs), a novel subclass of partially observable Markov decision processes (POMDPs) that incorporates the theory of missing data. A miss-MDP is a POMDP whose observation function is a missingness function,…

Asymptotic properties of three estimators of probability density function of sample maximum $f_{(m)}:=mfF^{m-1}$ are derived, where $m$ is a function of sample size $n$. One of the estimators is the parametrically fitted by the…

Statistics Theory · Mathematics 2022-06-13 Taku Moriyama

The monotone minimal perfect hash function (MMPHF) problem is the following indexing problem. Given a set $S= \{s_1,\ldots,s_n\}$ of $n$ distinct keys from a universe $U$ of size $u$, create a data structure $DS$ that answers the following…

Data Structures and Algorithms · Computer Science 2022-07-26 Sepehr Assadi , Martin Farach-Colton , William Kuszmaul

In this report, we show that all n-variable Boolean function can be represented as polynomial threshold functions (PTF) with at most $0.75 \times 2^n$ non-zero integer coefficients and give an upper bound on the absolute value of these…

Discrete Mathematics · Computer Science 2020-07-07 Erhan Oztop , Minoru Asada

Two semimetrics on probability distributions are proposed, given as the sum of differences of expectations of analytic functions evaluated at spatial or frequency locations (i.e, features). The features are chosen so as to maximize the…

Machine Learning · Statistics 2016-10-31 Wittawat Jitkrittum , Zoltan Szabo , Kacper Chwialkowski , Arthur Gretton

In real life, we frequently come across data sets that involve some independent explanatory variable(s) generating a set of ordinal responses. These ordinal responses may correspond to an underlying continuous latent variable, which is…

Methodology · Statistics 2024-01-08 Arijit Pyne , Subhrajyoty Roy , Abhik Ghosh , Ayanendranath Basu

The MPE (Most Probable Explanation) query plays an important role in probabilistic inference. MPE solution algorithms for probabilistic relational models essentially adapt existing belief assessment method, replacing summation with…

Artificial Intelligence · Computer Science 2012-10-19 Udi Apsel , Ronen I. Brafman

In this paper we provide a new efficient algorithm for approximately computing the profile maximum likelihood (PML) distribution, a prominent quantity in symmetric property estimation. We provide an algorithm which matches the previous best…

Data Structures and Algorithms · Computer Science 2020-11-06 Nima Anari , Moses Charikar , Kirankumar Shiragur , Aaron Sidford

We prove uniform estimates for the expected value of averages of order statistics of bivariate functions in terms of their largest values by a direct analysis. As an application, uniform estimates for the expected value of averages of order…

Probability · Mathematics 2018-10-03 Richard Lechner , Markus Passenbrunner , Joscha Prochno

We consider a one-dimensional discrete symmetric random walk with a reflecting boundary at the origin. Generating functions are found for the 2- dimensional probability distribution P{Sn = x,max1?j?n Sn = a} of being at position x after n…

Probability · Mathematics 2013-05-27 Jerome K. Percus , Ora E. Percus

The main objective of this paper is to look from the unique point of view at some phenomena arising in different areas of probability theory and mathematical statistics. We will try to understand what is common between classical…

Probability · Mathematics 2012-03-01 Oleg Lepski