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In this paper, first we have defined a uniform distribution on the boundary of a regular hexagon, and then investigated the optimal sets of $n$-means and the $n$th quantization errors for all positive integers $n$. We give an exact formula…

The basic goal of quantization for probability distribution is to reduce the number of values, which is typically uncountable, describing a probability distribution to some finite set and thus to make an approximation of a continuous…

Probability · Mathematics 2022-08-23 Joseph Rosenblatt , Mrinal Kanti Roychowdhury

In this paper, we have considered a uniform distribution on a regular polygon with $k$-sides for some $k\geq 3$ and the set of all its $k$ vertices as a conditional set. For the uniform distribution under the conditional set first, for all…

Probability · Mathematics 2025-05-21 Christina Hamilton , Evans Nyanney , Megha Pandey , Mrinal K. Roychowdhury

Quantization for a Borel probability measure refers to the idea of estimating a given probability by a discrete probability with support containing a finite number of elements. If in the quantization some of the elements in the support are…

Probability · Mathematics 2025-03-24 Pigar Biteng , Mathieu Caguiat , Tsianna Dominguez , Mrinal Kanti Roychowdhury

We approximate the uniform measure on an equilateral triangle by a measure supported on $n$ points. We find the optimal sets of points ($n$-means) and corresponding approximation (quantization) error for $n\leq4$, give numerical…

Information Theory · Computer Science 2017-02-16 Carl P. Dettmann , Mrinal Kanti Roychowdhury

The basic goal of quantization for probability distribution is to reduce the number of values, which is typically uncountable, describing a probability distribution to some finite set and thus approximation of a continuous probability…

Probability · Mathematics 2021-01-27 Mrinal Kanti Roychowdhury

The basic goal of quantization for probability distribution is to reduce the number of values, which is typically uncountable, describing a probability distribution to some finite set and thus approximation of a continuous probability…

Probability · Mathematics 2020-07-03 Mrinal Kanti Roychowdhury , Wasiela Salinas

Quantization for a probability distribution refers to the idea of estimating a given probability by a discrete probability supported by a finite number of points. In this paper, firstly a general approach to this process is outlined using…

Probability · Mathematics 2022-01-26 Joseph Rosenblatt , Mrinal Kanti Roychowdhury

This article presents some interesting and novel results concerning the average modulus of random polynomials on the unit circle and the unit disc, with coefficients distributed as standard normal variates. The paper also introduces new…

Complex Variables · Mathematics 2026-05-19 Sajad A. Sheikh , Mohd. Ibrahim Mir

Bucklew and Wise (1982) showed that the quantization dimension of an absolutely continuous probability measure on a given Euclidean space is constant and equals the Euclidean dimension of the space, and the quantization coefficient exists…

Probability · Mathematics 2025-07-23 Evans Nyanney , Megha Pandey , Mrinal Kanti Roychowdhury

In this paper, we first consider a family of constraints given by straight lines. For a uniform probability distribution, we determine the constrained optimal sets of $n$-points and the corresponding $n$th constrained quantization errors…

Probability · Mathematics 2025-09-26 Pavjeet Singh , S. K. Katiyar , Megha Pandey , Mrinal K. Roychowdhury

We introduce the boolean convolution for probability measures on the unit circle. Roughly speaking, it describes the distribution of the product of two boolean independent unitary random variables. We find an analogue of the characteristic…

Functional Analysis · Mathematics 2009-06-13 Uwe Franz

This expository paper provides a unified and pedagogical introduction to optimal quantization for probability measures supported on spherical curves and discrete subsets of the sphere, emphasizing both continuous and discrete settings. We…

Optimization and Control · Mathematics 2025-12-25 Mrinal Kanti Roychowdhury

For a sample of absolutely bounded i.i.d. random variables with a continuous density the cumulative distribution function of the sample variance is represented by a univariate integral over a Fourier series. If the density is a polynomial…

Statistics Theory · Mathematics 2008-10-10 T. Royen

We examine a generalization of the binomial distribution associated with a strictly increasing sequence of numbers and we prove its Poisson-like limit. Such generalizations might be found in quantum optics with imperfect detection. We…

Mathematical Physics · Physics 2015-05-28 E. M. F. Curado , J. P. Gazeau , Ligia M. C. S. Rodrigues

We characterize the power of constant-depth Boolean circuits in generating uniform symmetric distributions. Let $f\colon\{0,1\}^m\to\{0,1\}^n$ be a Boolean function where each output bit of $f$ depends only on $O(1)$ input bits. Assume the…

Computational Complexity · Computer Science 2025-02-27 Daniel M. Kane , Anthony Ostuni , Kewen Wu

A generalization of the quotient integral formula is presented and some of its properties are investigated. Also the relations between two function spaces related to the spacial homogeneous spaces are derived by using general quotient…

Representation Theory · Mathematics 2017-02-22 T. Derikvand , R. A. Kamyabi-Gol , M. Janfada

We develop a notion of quantum observable for the general boundary formulation of quantum theory. This notion is adapted to spacetime regions rather than to hypersurfaces and naturally fits into the topological quantum field theory like…

High Energy Physics - Theory · Physics 2012-09-06 Robert Oeckl

Constrained quantization for a Borel probability measure refers to the idea of estimating a given probability by a discrete probability with a finite number of supporting points lying on a specific set. The specific set is known as the…

Let X be a real or complex Hilbert space of finite but large dimension d, let S(X) denote the unit sphere of X, and let u denote the normalized uniform measure on S(X). For a finite subset B of S(X), we may test whether it is approximately…

Probability · Mathematics 2019-08-01 Sheldon Goldstein , Joel L. Lebowitz , Roderich Tumulka , Nino Zanghi
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