### Abstract

Bayesian analysis is an alternative approach in statistical inferences. The inclusion of other information regarding the parameter of the model is one of analysis capabilities. In the area of extreme rainfall analysis, expert opinion can be used as prior information to model the extreme events. Thus, considering previous or expert knowledge about the parameter of interest would reduce the uncertainty of the model. In this study, the annual maximum (AM) rainfall data of Alor Setar rain gauge station is modeled by the Generalized Extreme Value (GEV) distribution. A Bayesian Markov Chain Monte Carlo (MCMC) simulation is used for parameter estimation. Comparison of the outcomes between non-informative and informative priors is our main interest. The results show that there is a reduction in estimated values, which is due to informative priors.

Original language | English |
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Title of host publication | 2014 UKM FST Postgraduate Colloquium - Proceedings of the Universiti Kebangsaan Malaysia, Faculty of Science and Technology 2014 Postgraduate Colloquium |

Editors | Zahari Ibrahim, Haja Maideen Kader Maideen, Nazlina Ibrahim, Nurul Huda Abd Karim, Taufik Yusof, Fatimah Abdul Razak, Nurulkamal Maseran, Rozida Mohd Khalid, Noor Baa'yah Ibrahim, Hasidah Mohd. Sidek, Mohd Salmi Md Noorani, Norbert Simon |

Publisher | American Institute of Physics Inc. |

Pages | 913-917 |

Number of pages | 5 |

ISBN (Electronic) | 9780735412507 |

DOIs | |

Publication status | Published - 1 Jan 2014 |

Event | 2014 Postgraduate Colloquium of the Universiti Kebangsaan Malaysia, Faculty of Science and Technology, UKM FST 2014 - Selangor, Malaysia Duration: 9 Apr 2014 → 11 Apr 2014 |

### Publication series

Name | AIP Conference Proceedings |
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Volume | 1614 |

ISSN (Print) | 0094-243X |

ISSN (Electronic) | 1551-7616 |

### Other

Other | 2014 Postgraduate Colloquium of the Universiti Kebangsaan Malaysia, Faculty of Science and Technology, UKM FST 2014 |
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Country | Malaysia |

City | Selangor |

Period | 9/4/14 → 11/4/14 |

### Fingerprint

### Keywords

- Bayesian mcmc
- Extreme rainfall analysis
- Informative priors

### ASJC Scopus subject areas

- Physics and Astronomy(all)

### Cite this

*2014 UKM FST Postgraduate Colloquium - Proceedings of the Universiti Kebangsaan Malaysia, Faculty of Science and Technology 2014 Postgraduate Colloquium*(pp. 913-917). (AIP Conference Proceedings; Vol. 1614). American Institute of Physics Inc.. https://doi.org/10.1063/1.4895323

**Bayesian extreme rainfall analysis using informative prior : A case study of alor setar.** / Eli, Annazirin; Wan Zin @ Wan Ibrahim, Wan Zawiah; Ibrahim, Kamarulzaman; Jemain, Abdul Aziz.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*2014 UKM FST Postgraduate Colloquium - Proceedings of the Universiti Kebangsaan Malaysia, Faculty of Science and Technology 2014 Postgraduate Colloquium.*AIP Conference Proceedings, vol. 1614, American Institute of Physics Inc., pp. 913-917, 2014 Postgraduate Colloquium of the Universiti Kebangsaan Malaysia, Faculty of Science and Technology, UKM FST 2014, Selangor, Malaysia, 9/4/14. https://doi.org/10.1063/1.4895323

}

TY - GEN

T1 - Bayesian extreme rainfall analysis using informative prior

T2 - A case study of alor setar

AU - Eli, Annazirin

AU - Wan Zin @ Wan Ibrahim, Wan Zawiah

AU - Ibrahim, Kamarulzaman

AU - Jemain, Abdul Aziz

PY - 2014/1/1

Y1 - 2014/1/1

N2 - Bayesian analysis is an alternative approach in statistical inferences. The inclusion of other information regarding the parameter of the model is one of analysis capabilities. In the area of extreme rainfall analysis, expert opinion can be used as prior information to model the extreme events. Thus, considering previous or expert knowledge about the parameter of interest would reduce the uncertainty of the model. In this study, the annual maximum (AM) rainfall data of Alor Setar rain gauge station is modeled by the Generalized Extreme Value (GEV) distribution. A Bayesian Markov Chain Monte Carlo (MCMC) simulation is used for parameter estimation. Comparison of the outcomes between non-informative and informative priors is our main interest. The results show that there is a reduction in estimated values, which is due to informative priors.

AB - Bayesian analysis is an alternative approach in statistical inferences. The inclusion of other information regarding the parameter of the model is one of analysis capabilities. In the area of extreme rainfall analysis, expert opinion can be used as prior information to model the extreme events. Thus, considering previous or expert knowledge about the parameter of interest would reduce the uncertainty of the model. In this study, the annual maximum (AM) rainfall data of Alor Setar rain gauge station is modeled by the Generalized Extreme Value (GEV) distribution. A Bayesian Markov Chain Monte Carlo (MCMC) simulation is used for parameter estimation. Comparison of the outcomes between non-informative and informative priors is our main interest. The results show that there is a reduction in estimated values, which is due to informative priors.

KW - Bayesian mcmc

KW - Extreme rainfall analysis

KW - Informative priors

UR - http://www.scopus.com/inward/record.url?scp=85063853541&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85063853541&partnerID=8YFLogxK

U2 - 10.1063/1.4895323

DO - 10.1063/1.4895323

M3 - Conference contribution

T3 - AIP Conference Proceedings

SP - 913

EP - 917

BT - 2014 UKM FST Postgraduate Colloquium - Proceedings of the Universiti Kebangsaan Malaysia, Faculty of Science and Technology 2014 Postgraduate Colloquium

A2 - Ibrahim, Zahari

A2 - Maideen, Haja Maideen Kader

A2 - Ibrahim, Nazlina

A2 - Karim, Nurul Huda Abd

A2 - Yusof, Taufik

A2 - Razak, Fatimah Abdul

A2 - Maseran, Nurulkamal

A2 - Khalid, Rozida Mohd

A2 - Ibrahim, Noor Baa'yah

A2 - Sidek, Hasidah Mohd.

A2 - Noorani, Mohd Salmi Md

A2 - Simon, Norbert

PB - American Institute of Physics Inc.

ER -