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  1. 1
    Modeling Earthquake Bond Prices with Correlated Dual Trigger Indices and the Approximate Solution Using the Monte Carlo Algorithm

    Modeling Earthquake Bond Prices with Correlated Dual Trigger Indices and the Approximate Solution Using the Monte Carlo Algorithm by Riza Andrian Ibrahim, Sukono, Herlina Napitupulu, Rose Irnawaty Ibrahim

    Published 2025
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  2. 2
    Hierarchical Bayesian estimation for stationary autoregressive models using reversible jump MCMC algorithm

    Hierarchical Bayesian estimation for stationary autoregressive models using reversible jump MCMC algorithm by Suparman, S., Rusiman, Mohd Saifullah

    Published 2018
    “…The posterior distribution has a complex shape so that the Bayesian estimator is not analytically determined. The reversible jump Markov Chain Monte Carlo (MCMC) algorithm is proposed to obtain the Bayesian estimator. …”
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  3. 3
    Bayesian survival and hazard estimates for Weibull regression with censored data using modified Jeffreys prior

    Bayesian survival and hazard estimates for Weibull regression with censored data using modified Jeffreys prior by Ahmed, Al Omari Mohammed

    Published 2013
    “…Subsequently, we use Markov Chain Monte Carlo (MCMC) method in the Bayesian estimator of the Weibull distributionand Weibull regression model with shape unknown. …”
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    Thesis
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  4. 4
    Simple algorithm for estimating the effects of reinforcement loadings and arrangements on composite properties

    Simple algorithm for estimating the effects of reinforcement loadings and arrangements on composite properties by Wan Ibrahim, W. M. A, Yeoh, C. K., Teh, P.L., Khairel Rafezi, Ahmad, Ruhiyuddin , Mohd Zaki

    Published 2010
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  5. 5
    Slice sampling technique in Bayesian extreme of gold price modelling

    Slice sampling technique in Bayesian extreme of gold price modelling by Rostami, Mohammad, Adam, Mohd Bakri, Ibrahim, Noor Akma, Yahya, Mohamed Hisham

    Published 2013
    “…We compared the accuracy of slice sampling with other methods for a Gumbel model. This study revealed that slice sampling algorithm offers more accurate and closer estimates with less RMSE than other methods. …”
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  6. 6
    Estimation of the Epidemiological Parameter for the COVID-19 Outbreak

    Estimation of the Epidemiological Parameter for the COVID-19 Outbreak by Muhammad Fahmi, Ahmad Zuber, Norhayati, Rosli, Noryanti, Muhammad

    Published 2024
    “…In this study, we propose a Metropolis-Hastings algorithm of the Markov Chain Monte Carlo (MCMC) method to estimate the epidemiological parameters of infectious rate, fatality rate, recovery rate, and reproduction numbers. …”
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  7. 7
    Detection of multiple outliners in linear regression using nonparametric methods

    Detection of multiple outliners in linear regression using nonparametric methods by Adnan, Robiah

    Published 2004
    “…REFERENCES Agullo, J. (2000). New Algorithms for Computing the Least Trimmed Squares Regression Estimator. …”
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    Monograph
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  8. 8
    Estimation of Information Measures for Power-Function Distribution in Presence of Outliers and Their Applications

    Estimation of Information Measures for Power-Function Distribution in Presence of Outliers and Their Applications by Hassan, Amal Soliman, Elsherpieny, Elsayed Ahmed, Mohamed, Rokaya Elmorsy

    Published 2022
    “…The Bayesian estimators were computed empirically using a Monte Carlo simulation based on the Gibbs sampling algorithm. …”
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  9. 9
    Statistical approach on grading: mixture modeling

    Statistical approach on grading: mixture modeling by Md. Desa, Zairul Nor Deana

    Published 2006
    “…A solution to this problem is using the Markov Chain Monte Carlo method namely Gibbs sampler algorithm. The Gibbs sampler algorithm is applied using the WinBUGS programming package. …”
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  10. 10
    Entropy in portfolio optimization / Yasaman Izadparast Shirazi

    Entropy in portfolio optimization / Yasaman Izadparast Shirazi by Yasaman Izadparast, Shirazi

    Published 2017
    “…The failure of this technique has been investigated and an adaptive beta-divergent method is proposed to ensure robust estimation. The usefulness of this technique has been verified with Monte-Carlo simulation in the context of portfolio analysis. …”
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  11. 11
    Satellite attitude determination utilizing measurement sensor data and kalman filtering

    Satellite attitude determination utilizing measurement sensor data and kalman filtering by Samaan, Malak A., Abdelrahman, Mohammad

    Published 2006
    “…This assessment was done by using Monte Carlo methods to simulate these sensors. Using only star measurements an optimal satellite orientation estimate is found using the method of least squares, and the particular algorithm invoked is referred to ESOQ2 method. …”
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  12. 12
    Parameter-driven count time series models / Nawwal Ahmad Bukhari

    Parameter-driven count time series models / Nawwal Ahmad Bukhari by Nawwal , Ahmad Bukhari

    Published 2018
    “…Simulation shows that MCEM algorithm and particle method are useful for the parameter estimation of the Poisson model. …”
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  13. 13
    Grouping based radio frequency identification anti-collision protocols for dense internet of things application

    Grouping based radio frequency identification anti-collision protocols for dense internet of things application by Umelo, Nnamdi H., Noordin, Nor K., A. Rasid, Mohd Fadlee, Geok, Tan K., Hashim, Fazirulhisyam

    Published 2022
    “…This paper analyzes selected grouping-based algorithms. Their underlining principles are discussed including their tag estimation methods. …”
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  14. 14
    A Bayesian parameter learning procedure for nonlinear dynamical systems via the ensemble Kalman filter

    A Bayesian parameter learning procedure for nonlinear dynamical systems via the ensemble Kalman filter by Ur Rehman, M.J., Dass, S.C., Asirvadam, V.S.

    Published 2018
    “…Based on observations made from stochastic dynamical systems, we consider the issue of parameter learning, and a related state estimation problem. We develop a Markov Chain Monte Carlo (MCMC) algorithm, which is an iterative method, for parameter inference. …”
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  15. 15
    Estimated and analysis of the relationship between the endogenous and exogenous variables using fuzzy semi-paranetric sample selection model

    Estimated and analysis of the relationship between the endogenous and exogenous variables using fuzzy semi-paranetric sample selection model by MuhamadSafiih, L, Kamil, A.A., Abu Osman, M.T.

    Published 2014
    “…The effectiveness of the estimators for this model is investigated. Monte Carlo simulation revealed that consistency depends on bandwidth parameter. …”
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  16. 16
    Extreme air pollutant data analysis using classical and Bayesian approaches

    Extreme air pollutant data analysis using classical and Bayesian approaches by Mohd Amin, Nor Azrita

    Published 2015
    “…MTM algorithm is an extension of MH algorithm, designed to improve the convergence of MH algorithm by performing parallel computation. …”
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  17. 17
    Subspace Techniques for Brain Signal Enhancement

    Subspace Techniques for Brain Signal Enhancement by Kamel , Nidal, Yusoff, Mohd Zuki

    Published 2009
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  18. 18
    The computation of confidence intervals for the state parameters of power systems

    The computation of confidence intervals for the state parameters of power systems by Khan, Z., Razali, R.B., Daud, H., Nor, N.M., Fotuhi-Firuzabad, M., Krebs, K.L.

    Published 2016
    “…Conclusions: The results of the study show that the method is effective and practically applicable in the state estimation of a power system. …”
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  19. 19
    The computation of confidence intervals for the state parameters of power systems

    The computation of confidence intervals for the state parameters of power systems by Khan, Z., Razali, R.B., Daud, H., Nor, N.M., Fotuhi-Firuzabad, M., Krebs, K.L.

    Published 2016
    “…Conclusions: The results of the study show that the method is effective and practically applicable in the state estimation of a power system. …”
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  20. 20
    Determining the order of a moving average model of time series using reversible jump MCMC: a comparison between laplacian and gaussian noises

    Determining the order of a moving average model of time series using reversible jump MCMC: a comparison between laplacian and gaussian noises by Suparman, Suparman, Abdellah Salhi, Abdellah Salhi, Rusiman, Mohd Saifullah

    Published 2020
    “…The results showed that the MCMC algorithm can estimate the parameters of the MA model. …”
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