Temat: Metropolis-Hastings - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Metropolis-Hastings method for Wohler curve parameter identification
Autorzy:: Woch, M.
Corbetta, M.
Giglio, M.
Sbarufatti, C.
Kamiński, G.
Powiązania:: https://bibliotekanauki.pl/articles/2069517.pdf
Data publikacji:: 2014
Wydawca:: Uniwersytet Morski w Gdyni. Polskie Towarzystwo Bezpieczeństwa i Niezawodności
Tematy:: Wohler curve
Metropolis-Hastings
probabilistic fatigue
Opis:: In this work, the Metropolis-Hastings sampling technique has been used for the parameter identification of Wohler curve of aluminium alloy 2024-T4. The Metropolis-Hasting algorithm is one of the most widespread Markov chain Monte Carlo methods for posterior distribution estimation, and it is presented with an adaptive formulation to estimate the probability density functions of Wohler parameters. Results are presented in terms of distribution shape and parameter correlations. The information about parameter distributions of Wohler equation is useful to prepare risk analyses based on statistical safe life approach.
Źródło:: Journal of Polish Safety and Reliability Association; 2014, 5, 2; 143--150
2084-5316
Pojawia się w:: Journal of Polish Safety and Reliability Association
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Comparison evolutionary algorithms with Metropolis-Hastings method exemplified by high-fatigue Wohler curve parameter identification
Autorzy:: Woch, M.
Powiązania:: https://bibliotekanauki.pl/articles/2069092.pdf
Data publikacji:: 2015
Wydawca:: Uniwersytet Morski w Gdyni. Polskie Towarzystwo Bezpieczeństwa i Niezawodności
Tematy:: Wohler curve
Metropolis-Hastings
evolutionary algorithms probabilistic
fatigue
Opis:: In this work, evolutionary algorithms together with the Metropolis-Hastings sampling technique have been used for parameter identification of the Wohler curve of duraluminum alloy 2024-T3. An evolutionary algorithm is a subset of evolutionary computation, a generic population-based metaheuristic optimization algorithm. The Metropolis-Hasting algorithm is one of the most widespread Markov chain Monte Carlo methods for posterior distribution estimation. In this contribution, both algorithms have been presented to estimate the probability density functions using Wohler parameters as a case study. Results were shown in terms of distribution shape and parameter correlations and the differences, arising from applied algorithms, have been compared. The information about parameter distributions of Wohler equation is useful to prepare risk analyses based on statistical safe life approach. The safe life approach can be met, for instance, in assessing the reliability of an aircraft.
Źródło:: Journal of Polish Safety and Reliability Association; 2015, 6, 2; 153--158
2084-5316
Pojawia się w:: Journal of Polish Safety and Reliability Association
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 3.

Tytuł:: Application of the Rasch model in categorical pedigree analysis using MCEM: I binary data
Autorzy:: Qian, G.
Huggins, R.
Loesch, D.
Powiązania:: https://bibliotekanauki.pl/articles/729756.pdf
Data publikacji:: 2004
Wydawca:: Uniwersytet Zielonogórski. Wydział Matematyki, Informatyki i Ekonometrii
Tematy:: pedigree analysis
binary data
MCEM algorithm
Metropolis-Hastings algorithm
Opis:: An extension of the Rasch model with correlated latent variables is proposed to model correlated binary data within families. The latent variables have the classical correlation structure of Fisher (1918) and the model parameters thus have genetic interpretations. The proposed model is fitted to data using a hybrid of the Metropolis-Hastings algorithm and the MCEM modification of the EM-algorithm and is illustrated using genotype-phenotype data on a psychological subtest in families where some members are affected by the genetic disorder fragile X. In addition, hypothesis testing and model selection methods based on the Wald statistic are discussed.
Źródło:: Discussiones Mathematicae Probability and Statistics; 2004, 24, 2; 255-280
1509-9423
Pojawia się w:: Discussiones Mathematicae Probability and Statistics
Dostawca treści:: Biblioteka Nauki

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