A STOCHASTIC MODEL FOR TIME CHANGES IN A BINARY DYADIC RELATION, WITH APPLICATION TO GROUP DYNAMICS.

Abstract

This thesis is concerned with the development of a stochastic model for analyzing time changes in a binary dyadic relation over a finite set of points. For purposes of drawing statistical inference in time the total relation R (A) on the set A is looked upon as an aggregate of its subrelations on subsets of the set A. The number of states in which a subrelation may be found, at any time, is very large; consequently three classification schemes are described to obtain a small number of mutually exclusive and exhaustive classes. Methods are presented to enumerate the number of subrelations of R (A) in various states at any time, and to count the number of transitions from one state to another in time. The process of change in the total relation over time is described as a timedependent process, and some statistical tests to determine the nature of the dependence are given. Certain aspects of the probability distributions of the random variables R sub ni (number of n-point subrelations of R (A) in state s'i) are discussed in the second part of the thesis. It is shown that the probability distributions of R sub ni may be approximated by Poisson distributions. Finally an application of the model to group dynamics is described and empirical examination of Markov properties is made for a particular set of data. (Author)

Document Details

Document Type
Technical Report
Publication Date
Jun 11, 1962
Accession Number
AD0619189

Entities

People

  • T. N. Bhargava

Organizations

  • Michigan State University

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Classification
  • Data Science
  • Dynamics
  • Group Dynamics
  • Information Science
  • Mathematics
  • Probability
  • Probability Distributions
  • Random Variables
  • Statistical Inference
  • Statistical Tests
  • Transitions

Fields of Study

  • Mathematics

Readers

  • Mathematical Modeling and Probability Theory.
  • Regression Analysis.
  • Theoretical Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms