### Abstract

method are very strong, based as they are in work of De Finetti and developed further by Goldstein. A Bayes linear model typically requires less specification than a corresponding probability model, and therefore, for a given amount of model building effort one can model a more complex situation. This paper aims to give the reader a brief insight into the Bayes linear methodology. This will be done by briefly discussing the philosophy of the approach, the theory of the approach, highlighting some benefits and limitations of the approach and

ending with a brief example displaying the capabilities of the approach.

Original language | English |
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Publication status | Published - 2007 |

Event | Mathematical Methods in Reliability - , United Kingdom Duration: 8 May 2007 → 10 May 2007 |

### Conference

Conference | Mathematical Methods in Reliability |
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Country | United Kingdom |

Period | 8/05/07 → 10/05/07 |

### Fingerprint

### Keywords

- Bayes linear methods
- Bayes linear methodology
- reliability management

### Cite this

*An introduction to Bayes linear methods*. Paper presented at Mathematical Methods in Reliability, United Kingdom.

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**An introduction to Bayes linear methods.** / Revie, Matthew; Bedford, Tim; Walls, Lesley.

Research output: Contribution to conference › Paper

TY - CONF

T1 - An introduction to Bayes linear methods

AU - Revie, Matthew

AU - Bedford, Tim

AU - Walls, Lesley

PY - 2007

Y1 - 2007

N2 - Bayesian methods are common in reliability and risk assessment, however, such methods can demand a large amount of specification, can be computationally intensive and hence impractical for practitioners to use. The Bayes linear methodology is similar in spirit to a Bayesian approach but offers an alternative method of carrying out inference. Bayes linear methods are based on the use of expected values rather than probabilities, and updating is carried out by linear adjustment rather than by Bayes Theorem. The foundations of themethod are very strong, based as they are in work of De Finetti and developed further by Goldstein. A Bayes linear model typically requires less specification than a corresponding probability model, and therefore, for a given amount of model building effort one can model a more complex situation. This paper aims to give the reader a brief insight into the Bayes linear methodology. This will be done by briefly discussing the philosophy of the approach, the theory of the approach, highlighting some benefits and limitations of the approach andending with a brief example displaying the capabilities of the approach.

AB - Bayesian methods are common in reliability and risk assessment, however, such methods can demand a large amount of specification, can be computationally intensive and hence impractical for practitioners to use. The Bayes linear methodology is similar in spirit to a Bayesian approach but offers an alternative method of carrying out inference. Bayes linear methods are based on the use of expected values rather than probabilities, and updating is carried out by linear adjustment rather than by Bayes Theorem. The foundations of themethod are very strong, based as they are in work of De Finetti and developed further by Goldstein. A Bayes linear model typically requires less specification than a corresponding probability model, and therefore, for a given amount of model building effort one can model a more complex situation. This paper aims to give the reader a brief insight into the Bayes linear methodology. This will be done by briefly discussing the philosophy of the approach, the theory of the approach, highlighting some benefits and limitations of the approach andending with a brief example displaying the capabilities of the approach.

KW - Bayes linear methods

KW - Bayes linear methodology

KW - reliability management

M3 - Paper

ER -