## Workshop in Analysis and Probability Seminar

**Date:** July 10, 2017

**Time:** 2:00PM - 2:50PM

**Location:** BLOC 220

**Speaker:** Mrinal Kanti Roychowdhury, The University of Texas Rio Grande Valley

**Title:** *An overview of optimal quantization*

**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. Recently, in the paper ``An overview of the quantization for mixed distributions'', available in arXiv, I have determined the optimal sets of \$n\$-means, the \$n\$th quantization error, and the quantization dimensions for different mixed distributions. Besides, I 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 of mixed distributions. I will talk about it.