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BAYESIAN INFERENCE APPLIED FOR PARAMETER ESTIMATION

Penulis : Akbar Kevin Maricar [13619001]
Kontributor / Dosen Pembimbing :
  • Dr. -Ing. Ir. Javensius Sembiring, S.T, M.T.
Jenis Koleksi : Tugas Akhir
Tahun Terbit :
Penerbit : Teknik Dirgantara
Fakultas : Fakultas Teknik Mesin dan Dirgantara
Subjek : Engineering & allied operations
Kata Kunci : Bayesian Inference, Least Square, Metropolis-Hastings, Parameter Estimation, System Identification
Sumber : Program Studi Teknik Dirgantara
Staf Input/Edit : Irwan Sofiyan   Alice Diniarti
File : 9 file
Tanggal Input : 15 Sep 2023

ABSTRAK Akbar Kevin Maricar
Terbatas  Irwan Sofiyan
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COVER - Akbar Kevin Maricar
Terbatas  Irwan Sofiyan
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BAB 1 - Akbar Kevin Maricar
Terbatas  Irwan Sofiyan
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BAB 2 - Akbar Kevin Maricar
Terbatas  Irwan Sofiyan
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BAB 3 - Akbar Kevin Maricar
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BAB 4 - Akbar Kevin Maricar
Terbatas  Irwan Sofiyan
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BAB 5 - Akbar Kevin Maricar
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BAB 6 - Akbar Kevin Maricar
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PUSTAKA - Akbar Kevin Maricar
Terbatas  Irwan Sofiyan
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In the realm of aircraft development, leveraging flight test data is pivotal for enhanced insights into performance and behaviour. This research revolves around System Identification as a fundamental facet of aircraft development. Investigating the potential of Bayesian Inference in Parameter Estimation, this research integrates Bayesian Inference following model structure determination via the Least Squares method, exclusively for parameter estimation. Employing the Metropolis-Hastings algorithm, the research conducts posterior distribution sampling within the Bayesian Inference framework. Additionally, the research encompasses Uncertainty Quantification, grounded in the analysis of parameter distributions. In sum, this study bridges System Identification, Bayesian Inference, and Uncertainty Quantification, contributing to a comprehensive framework for informed aircraft parameter estimation.

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