Bayesian Decision Making and Learning for Continuous-Time Markov Systems.

Abstract

The document is concerned with Bayesian decision making and learning algorithms for a particular problem in parametric pattern recognition in which each of a finite set of pattern classes is characterized by a continuous-time, discrete-state Markov process. The basic problem considered is that of determining rules for making decisions about the identity of the active pattern class based upon observation of a sample function in some finite interval. The stationary transition probability matrices for the processes in question are the parameters of the pattern classes. (Author)

Document Details

Document Type
Technical Report
Publication Date
Nov 16, 1970
Accession Number
AD0720810

Entities

People

  • Erdal Panayirci
  • Richard C. Dubes

Organizations

  • Michigan State University

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Identities
  • Intervals
  • Learning
  • Markov Processes
  • Mathematics
  • Observation
  • Pattern Recognition
  • Probability
  • Recognition
  • Stationary
  • Transitions

Fields of Study

  • Mathematics

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Speech Processing/Speech Recognition.
  • Statistical inference.

Technology Areas

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