Quantization for a Mixture of Uniform Distributions Associated with Probability Vectors

Authors

Mrinal Kanti Roychowdhury, The University of Texas Rio Grande Valley
Wasiela Salinas, The University of Texas Rio Grande Valley

Document Type

Article

Publication Date

7-23-2020

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

Comments

© 2020 Mrinal Kanti Roychowdhury et al., published by Sciendo

Recommended Citation

Roychowdhury, Mrinal Kanti and Salinas, Wasiela. "Quantization for a Mixture of Uniform Distributions Associated with Probability Vectors" Uniform distribution theory, vol.15, no.1, 2020, pp.105-142. https://doi.org/10.2478/udt-2020-0006

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.

Publication Title

Uniform distribution theory

DOI

10.2478/udt-2020-0006