INFERENCE IN STOCHASTIC PROCESSES

Abstract

The investigation was designed to prepare a monograph on certain mathematical aspects of the inference theory of stochastic processes, the principal components of which are substantially completed. These include substantive treatments of the foundations of inference theory, i.e., the projective limits of probability spaces, of conditional probability distributions and expectations, which occupy a central position in the analysis of essentially all the problems of inference, some new or simplified proofs of the standard theory of martingales together with a demonstration of the equivalence of the martingale convergence and the Andersen-Jessen theory, of stochastic difference and differential equations in both the physical and social sciences, of Gaussian processes, and of hypothesis testing, parametric estimation, and prediction, as the latter three topics relate to inference theory.

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 1970
Accession Number
AD0709223

Entities

People

  • M. M. Rao

Organizations

  • Carnegie Mellon University

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Abstracts
  • Data Science
  • Differential Equations
  • Equations
  • Functional Analysis
  • Gaussian Processes
  • Information Science
  • Markov Processes
  • Mathematics
  • Probability
  • Probability Distributions
  • Random Variables
  • Standards
  • Statistical Analysis
  • Stochastic Processes

Fields of Study

  • Mathematics

Readers

  • Mathematical Modeling and Probability Theory.
  • Theoretical Analysis.

Technology Areas

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