Accession Number:

ADA114622

Title:

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

Descriptive Note:

Technical summary rept.,

Corporate Author:

WISCONSIN UNIV-MADISON MATHEMATICS RESEARCH CENTER

Personal Author(s):

• Leonard,Tom

Report Date:

1982-02-01

Pagination or Media Count:

23.0

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.

Descriptors:

• *BAYES THEOREM
• *MARKOV PROCESSES
• *MODELS
• PREDICTIONS
• DISTRIBUTION
• POISSON DENSITY FUNCTIONS
• NORMAL DISTRIBUTION
• CONTROL
• CHARTS
• ONLINE SYSTEMS
• FORECASTING
• SYSTEMS ANALYSIS
• KALMAN FILTERING
• LINEAR REGRESSION ANALYSIS
• CHEMICAL ENGINEERING
• QUALITY CONTROL

Subject Categories:

• Industrial Chemistry and Chemical Processing
• Statistics and Probability

Distribution Statement:

APPROVED FOR PUBLIC RELEASE