Accession Number : AD0653877

Title :   RECONSTRUCTING PATTERNS FROM SAMPLE DATA.

Descriptive Note : Technical rept.,

Corporate Author : STANFORD UNIV CALIF DEPT OF STATISTICS

Personal Author(s) : Switzer,Paul

Report Date : 23 MAY 1967

Pagination or Media Count : 36

Abstract : A Euclidean region is partitioned in an unknown way into a known number of subregions. For a discrete set of points in the region we may observe into which subregion they each fall. The problem is to obtain an estimated reconstruction of the partition based on the observations. Section 1 outlines a probabilistic approach to the problem and defines criteria of goodness for an estimated reconstruction. Section 2 evaluates the performance of nearest-point rules. Section 3 examines the effect of sample-size and compares several arrangements of the data points. Section 4 proposes a modification of nearest-point procedures having a certain optimality property. Section 5 is more or less independent of the earlier sections; it explores several probabilistic partitioning models. (Author)

Descriptors :   (*SAMPLING, MATHEMATICAL MODELS), RANDOM VARIABLES, MATHEMATICS, THEOREMS, PROBABILITY, PATTERN RECOGNITION, STATISTICAL ANALYSIS

Subject Categories : Statistics and Probability

Distribution Statement : APPROVED FOR PUBLIC RELEASE