A Bayesian Approach to Markovian Models for Normal and Poisson Data.

Abstract

A Bayesian updating procedure is proposed for filtering the process parameters in the two-stage Markovian constant variance model for time varying normal data in the situation where the signal to noise ratio is unknown. A forecastign procedure is described which yields the entire predictive distribution of future observations; a numerical study involves an on-line analysis for chemical process concentration readings. A similar method is developed for Poisson data and applied to the analysis of an industrial control chart.

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Document Details

Document Type
Technical Report
Publication Date
Feb 01, 1982
Accession Number
ADA114622

Entities

People

  • Tom Leonard

Organizations

  • University of Wisconsin–Madison

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Bayes Theorem
  • Bayesian Networks
  • Contracts
  • Data Science
  • Information Science
  • Mathematical Analysis
  • Mathematical Filters
  • Mathematics
  • Models
  • Observation
  • Probabilistic Models
  • Probability
  • Probability Distributions
  • Random Variables
  • Statistical Analysis
  • Statistics
  • United States

Fields of Study

  • Mathematics

Readers

  • Computational Modeling and Simulation
  • Environmental Engineering.
  • Regression Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference