Applications of the Bayesian approach for experimentation and estimation

A Bayesian framework for systematic data collection and parameter estimation is proposed to aid experimentalists in effectively generating and interpreting data. The four stages of the Bayesian framework are: system description, system analysis, experimentation, and estimation. System description consists of specifying the system under investigation and collecting available information for the parameter estimation. Subsequently, system analysis entails a more in-depth system study by implementing various mathematical tools such as an observability and sensitivity analysis. The third stage in the framework is experimentation, consisting of experimental design, system calibration, and performing actual experiments. Finally, the last stage is estimation, where all relevant information and collected data is used for estimating the desired quantities. The Bayesian approach embedded within this framework provides a versatile, robust, and unified methodology allowing for consistent incorporation and propagation of uncertainty. To demonstrate the benefits, the Bayesian framework was applied to two different case studies of complex reaction engineering problems.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Chemical Engineering.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2006

Author dc:creator

• De Man, Patrick A. P. (Patrick Antonius Petrus)

Advisor dc:contributor.advisor

• Gregory J. McRae.

• Chemical Engineering.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/34624
• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Identifier URI

http://dspace.mit.edu/handle/1721.1/34624

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/34624