Quantization for a Mixture of Uniform Distributions Associated with Probability Vectors

Roychowdhury, Mrinal Kanti; Salinas, Wasiela

doi:10.2478/udt-2020-0006

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. Mixtures of probability distributions, also known as mixed distributions, are an exciting new area for optimal quantization. In this paper, we investigate the optimal quantization for three different mixed distributions generated by uniform distributions associated with probability vectors.

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Quantization for a Mixture of Uniform Distributions Associated with Probability Vectors

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