Validation of the thermal challenge problem using Bayesian Belief Networks.

doi:10.2172/875636

Title: Validation of the thermal challenge problem using Bayesian Belief Networks.

Full Record
Similar

Abstract

The thermal challenge problem has been developed at Sandia National Laboratories as a testbed for demonstrating various types of validation approaches and prediction methods. This report discusses one particular methodology to assess the validity of a computational model given experimental data. This methodology is based on Bayesian Belief Networks (BBNs) and can incorporate uncertainty in experimental measurements, in physical quantities, and model uncertainties. The approach uses the prior and posterior distributions of model output to compute a validation metric based on Bayesian hypothesis testing (a Bayes' factor). This report discusses various aspects of the BBN, specifically in the context of the thermal challenge problem. A BBN is developed for a given set of experimental data in a particular experimental configuration. The development of the BBN and the method for ''solving'' the BBN to develop the posterior distribution of model output through Monte Carlo Markov Chain sampling is discussed in detail. The use of the BBN to compute a Bayes' factor is demonstrated.

Authors:: McFarland, John; Swiler, Laura Painton

Publication Date:: Tue Nov 01 00:00:00 EST 2005

Research Org.:: Sandia National Laboratories

Sponsoring Org.:: USDOE

OSTI Identifier:: 875636

Report Number(s):: SAND2005-5980
TRN: US200603%%264

DOE Contract Number:: AC04-94AL85000

Resource Type:: Technical Report

Country of Publication:: United States

Language:: English

Subject:: 99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE; VALIDATION; FORECASTING; COMPUTER CODES; CALCULATION METHODS; Bayesian statistical decision theory.; Thermal analysis.

Citation Formats


                    McFarland, John, and Swiler, Laura Painton. Validation of the thermal challenge problem using Bayesian Belief Networks..  United States: N. p., 2005. 
        Web.  doi:10.2172/875636.

Copy to clipboard


                    McFarland, John, & Swiler, Laura Painton. Validation of the thermal challenge problem using Bayesian Belief Networks..  United States.  doi:10.2172/875636.

Copy to clipboard


                    McFarland, John, and Swiler, Laura Painton. Tue .  
        "Validation of the thermal challenge problem using Bayesian Belief Networks.".  United States.  
        doi:10.2172/875636.  https://www.osti.gov/servlets/purl/875636.

Copy to clipboard


                    
@article{osti_875636,

  title        = {Validation of the thermal challenge problem using Bayesian Belief Networks.},

  author       = {McFarland, John and Swiler, Laura Painton},

  abstractNote = {The thermal challenge problem has been developed at Sandia National Laboratories as a testbed for demonstrating various types of validation approaches and prediction methods. This report discusses one particular methodology to assess the validity of a computational model given experimental data. This methodology is based on Bayesian Belief Networks (BBNs) and can incorporate uncertainty in experimental measurements, in physical quantities, and model uncertainties. The approach uses the prior and posterior distributions of model output to compute a validation metric based on Bayesian hypothesis testing (a Bayes' factor). This report discusses various aspects of the BBN, specifically in the context of the thermal challenge problem. A BBN is developed for a given set of experimental data in a particular experimental configuration. The development of the BBN and the method for ''solving'' the BBN to develop the posterior distribution of model output through Monte Carlo Markov Chain sampling is discussed in detail. The use of the BBN to compute a Bayes' factor is demonstrated.},

  doi          = {10.2172/875636},

  journal      = {},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {Tue Nov 01 00:00:00 EST 2005},

  month        = {Tue Nov 01 00:00:00 EST 2005}

}

Copy to clipboard

Technical Report:

View Technical Report

DOI: 10.2172/875636

Save / Share:

Export Metadata

Save to My Library

A solution to the static frame validation challenge problem using Bayesian model selection

Grigoriu, M. D. ; Field, R. V. - Computer Methods in Applied Mechanics and Engineering

Within this paper, we provide a solution to the static frame validation challenge problem (see this issue) in a manner that is consistent with the guidelines provided by the Validation Challenge Workshop tasking document. The static frame problem is constructed such that variability in material properties is known to be the only source of uncertainty in the system description, but there is ignorance on the type of model that best describes this variability. Hence both types of uncertainty, aleatoric and epistemic, are present and must be addressed. Our approach is to consider a collection of competing probabilistic models for themore »« less
DOI: 10.1016/j.cma.2007.09.023

Full Text Available
Assessing land-use impacts on viability of intermountain trouts using Bayesian belief networks

Lee, D.C.

Many salmonid populations of the intermountain region of North America are threatened by habitat degradation resulting from changing land-use practices. Public lands managed by the USDA Forest Service and USDI Bureau of Land Management comprise a significant portion of the remaining suitable habitat. Public land-use decisions must consider the potential effects on sensitive species. A computer-aided viability assessment model is presented which can help anticipate impacts of management actions on resident trout populations. Central to this system is a Bayesian belief network that links physical habitat characteristics directly to reproduction and survival parameters of a population viability model. Recognizing thatmore »« less
Sensor fault diagnosis using Bayesian belief networks

Aradhye, H.B. ; Heger, A.S. - Transactions of the American Nuclear Society

This paper describes a method based on Bayesian belief networks (BBNs) sensor fault detection, isolation, classification, and accommodation (SFDIA). For this purpose, a BBN uses three basic types of nodes to represent the information associated with each sensor: (1) sensor-reading nodes that represent the mechanisms by which the information is communicated to the BBN, (2) sensor-status nodes that convey the status of the corresponding sensors at any given time, and (3) process-variable nodes that are a conceptual representation of the actual values of the process variables, which are unknown.
THREAT ANTICIPATION AND DECEPTIVE REASONING USING BAYESIAN BELIEF NETWORKS

Allgood, Glenn O ; Olama, Mohammed M ; Lake, Joe E

Recent events highlight the need for tools to anticipate threats posed by terrorists. Assessing these threats requires combining information from disparate data sources such as analytic models, simulations, historical data, sensor networks, and user judgments. These disparate data can be combined in a coherent, analytically defensible, and understandable manner using a Bayesian belief network (BBN). In this paper, we develop a BBN threat anticipatory model based on a deceptive reasoning algorithm using a network engineering process that treats the probability distributions of the BBN nodes within the broader context of the system development process.
Evaluation of risk from acts of terrorism :the adversary/defender model using belief and fuzzy sets.

Darby, John L.

Risk from an act of terrorism is a combination of the likelihood of an attack, the likelihood of success of the attack, and the consequences of the attack. The considerable epistemic uncertainty in each of these three factors can be addressed using the belief/plausibility measure of uncertainty from the Dempster/Shafer theory of evidence. The adversary determines the likelihood of the attack. The success of the attack and the consequences of the attack are determined by the security system and mitigation measures put in place by the defender. This report documents a process for evaluating risk of terrorist acts using anmore »« less
DOI: 10.2172/893554

Full Text Available