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MODELLING EXPERT JUDGEMENT INTO A BAYESIAN BELIEF NETWORK. A METHOD FOR CONSISTENT AND ROBUST DETERMINATION OF CONDITIONAL PROBABILITY TABLES

Hansson, Frida and Sjökvist, Stina (2013) FMS820 20131
Mathematical Statistics
Abstract (Swedish)
In project RASTEP (RApid Source TErm Prediction) a computerized tool for real time
prediction of source terms at a nuclear power plant is developed. The tool consists of two
modules where one is a Bayesian belief network (BBN). A BBN consists of connected nodes
and each node has a defined conditional probability table (CPT), which contains the
probabilities that a node is in its different states given the states of the node's immediate
predecessors. Due to the lack of data the CPTs for some nodes are subjectively determined
by experts in the field. Expert judgment may induce uncertainties in the network and it is
desirable to know how a relevant and defendable set of conditional probabilities in a BBN
can be defined.
This Master... (More)
In project RASTEP (RApid Source TErm Prediction) a computerized tool for real time
prediction of source terms at a nuclear power plant is developed. The tool consists of two
modules where one is a Bayesian belief network (BBN). A BBN consists of connected nodes
and each node has a defined conditional probability table (CPT), which contains the
probabilities that a node is in its different states given the states of the node's immediate
predecessors. Due to the lack of data the CPTs for some nodes are subjectively determined
by experts in the field. Expert judgment may induce uncertainties in the network and it is
desirable to know how a relevant and defendable set of conditional probabilities in a BBN
can be defined.
This Master Thesis is part of a R&D project run by Scandpower on behalf of the Nordic
Nuclear Safety Research (NKS). The aim of the thesis is to develop a general method where
experts' beliefs can be included in a systematic way when defining the CPTs in the BBN. The
proposed method consists of four parts; Network structure, Probability estimation, Sensitivity
analysis and Verification and validation. These parts are performed iteratively until the
network is robust and reliable. The main focus of the thesis is on the two parts Probability
estimation and Sensitivity analysis.
From literature different elicitation methods to help the experts assess probabilities in a CPT
were found. Two types of elicitation methods were studied; elicitation of a single probability
and elicitation of a full CPT. The method preferred when eliciting a single probability was
Probability scale since it is an easy and straightforward method for the expert to use.
Understanding and implementing methods for generating full CPTs required more attention
and were tested both on nodes for example networks and for a network developed in
RASTEP. The Likelihood method showed the best result for elicitation of a full CPT and this
method is beneficial to use when the expert is uncomfortable at expressing his beliefs as
probabilities.
An important outcome of the work performed, is that rough estimates of the probabilities are
sufficient as a first assignment since a sensitivity analysis will reveal which probabilities have
significant effect on the network's output and thus need to be more accurately assessed. The
sensitivity analysis also shows the constructor of the BBN how observable nodes, given
evidence, influence the network and may lead to modifications in the network's structure. (Less)
Please use this url to cite or link to this publication:
author
Hansson, Frida and Sjökvist, Stina
supervisor
organization
course
FMS820 20131
year
type
H2 - Master's Degree (Two Years)
subject
language
English
id
3866733
date added to LUP
2013-06-20 11:01:57
date last changed
2013-06-20 11:01:57
@misc{3866733,
  abstract     = {In project RASTEP (RApid Source TErm Prediction) a computerized tool for real time
prediction of source terms at a nuclear power plant is developed. The tool consists of two
modules where one is a Bayesian belief network (BBN). A BBN consists of connected nodes
and each node has a defined conditional probability table (CPT), which contains the
probabilities that a node is in its different states given the states of the node's immediate
predecessors. Due to the lack of data the CPTs for some nodes are subjectively determined
by experts in the field. Expert judgment may induce uncertainties in the network and it is
desirable to know how a relevant and defendable set of conditional probabilities in a BBN
can be defined.
This Master Thesis is part of a R&D project run by Scandpower on behalf of the Nordic
Nuclear Safety Research (NKS). The aim of the thesis is to develop a general method where
experts' beliefs can be included in a systematic way when defining the CPTs in the BBN. The
proposed method consists of four parts; Network structure, Probability estimation, Sensitivity
analysis and Verification and validation. These parts are performed iteratively until the
network is robust and reliable. The main focus of the thesis is on the two parts Probability
estimation and Sensitivity analysis.
From literature different elicitation methods to help the experts assess probabilities in a CPT
were found. Two types of elicitation methods were studied; elicitation of a single probability
and elicitation of a full CPT. The method preferred when eliciting a single probability was
Probability scale since it is an easy and straightforward method for the expert to use.
Understanding and implementing methods for generating full CPTs required more attention
and were tested both on nodes for example networks and for a network developed in
RASTEP. The Likelihood method showed the best result for elicitation of a full CPT and this
method is beneficial to use when the expert is uncomfortable at expressing his beliefs as
probabilities.
An important outcome of the work performed, is that rough estimates of the probabilities are
sufficient as a first assignment since a sensitivity analysis will reveal which probabilities have
significant effect on the network's output and thus need to be more accurately assessed. The
sensitivity analysis also shows the constructor of the BBN how observable nodes, given
evidence, influence the network and may lead to modifications in the network's structure.},
  author       = {Hansson, Frida and Sjökvist, Stina},
  language     = {eng},
  note         = {Student Paper},
  title        = {MODELLING EXPERT JUDGEMENT INTO A BAYESIAN BELIEF NETWORK. A METHOD FOR CONSISTENT AND ROBUST DETERMINATION OF CONDITIONAL PROBABILITY TABLES},
  year         = {2013},
}