OPTIMAL QUANTIZATION FOR MIXED DISTRIBUTIONS

IF 0.1 Q4 MATHEMATICS Real Analysis Exchange Pub Date : 2021-11-01 DOI:10.14321/REALANALEXCH.46.2.0451
M. Roychowdhury
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引用次数: 6

Abstract

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 distribution by a discrete distribution. Mixed distributions are an exciting new area for optimal quantization. In this paper, we have determined the optimal sets of n-means, the nth quantization errors, and the quantization dimensions of different mixed distributions. Besides, we have discussed whether the quantization coefficients for the mixed distributions exist. The results in this paper will give a motivation and insight into more general problems in quantization for mixed distributions.
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混合分布的最优量化
概率分布量化的基本目标是减少值的数量,这通常是不可计数的,将概率分布描述为某个有限集,从而通过离散分布近似连续概率分布。混合分布是优化量化的一个令人兴奋的新领域。在本文中,我们确定了不同混合分布的n均值、n量化误差和量化维数的最优集合。此外,我们还讨论了混合分布的量化系数是否存在。本文的结果将为混合分布量子化中更普遍的问题提供动力和见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Real Analysis Exchange
Real Analysis Exchange MATHEMATICS-
CiteScore
0.40
自引率
50.00%
发文量
15
期刊最新文献
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