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References
- McLachlan, G.J., dan Basford, K., 1988,Mixture Models: Inference and Application to Clustering, New York, Marcel and Decker Inc.
- Ieren, T.G. dan Oguntunde, P.E., 2018,A Comparison between Maximum Likelihood andBayesian Estimation Methods for a Shape Parameterof the Weibull-Exponential Distribution, Asian Journal of Probability and Statistics,1(1): 1-12, 2018.
- Walpole, R.E., dan Myers, R.H., 1995, Ilmu Peluang dan Statistika untuk Insinyur dan Ilmuwan, diterjemahkan oleh RK Sembiring, Bandung, Institut Teknologi Bandung.
- Hazhiah, I.T., Sugito, S., dan Rahmawati, R., 2012, Estimasi Parameter Weibull Dua Parameter dengan Metode Bayes, Jurnal Gaussian, 1(1), 103-102.
- Bonald, T., dan Feuillet, M., 2011, Network Performance Analysis, United States, Wiley.
- Iriawan, N., 2001, Penaksiran Model Mixture Normal Univariabel: Suatu Pendekatan Metode Bayesian dengan MCMC, Prosiding Seminar Nasional dan Konferda VII Matematika Wilayah DIY dan Jawa Tengah, Yogyakarta.
- Herrhyanto, N., 2011, Statistika Matematis Lanjutan, Bandung, CV Pustaka Setia.
- Gamerman, D., 1995, Markov Chain Monte Carlo, London, Chapman & Nall.
- Paramita, Z.H., dan Iriawan, N., 2014, Analisis Reliabilitas Transformator (Trafo) di PT PLN APJ Surabaya Barat dengan Pendekatan Bayesian Mixture, Jurnal Sains dan Seni Pomits, 3(2), 2337-3520.
- Ntzoufras, I., 2009, Bayesian Modelling using WinBUGS, USA, Wiley.
- Iriawan, N., 2011, Pemodelan Mixture of Mixture dalam Pemilihan Portofolio, Prosiding Seminar Nasional Statistika Universitas Diponegoro, Semarang.
- Haryono, 1995, Proses Stokastik Terapan, Surabaya, Instutut Teknologi Sepuluh Nopember.
- Walsh, B., 2004, Markov Chain Monte Carlo and Gibbs Sampling, Lecture Note for EEB 581, version (26) April 2004.
- Casella, G., dan George, E. I., 1992, Explaining the Gibbs Sampler,The American Statistician, 46(3), 167-174.
- Iriawan, N., 2000, Computationally Intensive Approaches to Inference in Neo-Normal Linier Models, DoctoralThesis, Australia, CUT.
- Rasmussen, C.E., 2000, The Infinite Gaussian Mixture Model, Denmark, MIT Press.
- Panik, M.J., 2005, Advanced Statistics from An Elementary Point of View, United States, Elsevier Academic Press.
- Bolstad, W.M., 2007, Introduction to Bayesian Statistics 2ndEdition, Amerika, John Wiley & Sons. Inc.
References
McLachlan, G.J., dan Basford, K., 1988,Mixture Models: Inference and Application to Clustering, New York, Marcel and Decker Inc.
Ieren, T.G. dan Oguntunde, P.E., 2018,A Comparison between Maximum Likelihood andBayesian Estimation Methods for a Shape Parameterof the Weibull-Exponential Distribution, Asian Journal of Probability and Statistics,1(1): 1-12, 2018.
Walpole, R.E., dan Myers, R.H., 1995, Ilmu Peluang dan Statistika untuk Insinyur dan Ilmuwan, diterjemahkan oleh RK Sembiring, Bandung, Institut Teknologi Bandung.
Hazhiah, I.T., Sugito, S., dan Rahmawati, R., 2012, Estimasi Parameter Weibull Dua Parameter dengan Metode Bayes, Jurnal Gaussian, 1(1), 103-102.
Bonald, T., dan Feuillet, M., 2011, Network Performance Analysis, United States, Wiley.
Iriawan, N., 2001, Penaksiran Model Mixture Normal Univariabel: Suatu Pendekatan Metode Bayesian dengan MCMC, Prosiding Seminar Nasional dan Konferda VII Matematika Wilayah DIY dan Jawa Tengah, Yogyakarta.
Herrhyanto, N., 2011, Statistika Matematis Lanjutan, Bandung, CV Pustaka Setia.
Gamerman, D., 1995, Markov Chain Monte Carlo, London, Chapman & Nall.
Paramita, Z.H., dan Iriawan, N., 2014, Analisis Reliabilitas Transformator (Trafo) di PT PLN APJ Surabaya Barat dengan Pendekatan Bayesian Mixture, Jurnal Sains dan Seni Pomits, 3(2), 2337-3520.
Ntzoufras, I., 2009, Bayesian Modelling using WinBUGS, USA, Wiley.
Iriawan, N., 2011, Pemodelan Mixture of Mixture dalam Pemilihan Portofolio, Prosiding Seminar Nasional Statistika Universitas Diponegoro, Semarang.
Haryono, 1995, Proses Stokastik Terapan, Surabaya, Instutut Teknologi Sepuluh Nopember.
Walsh, B., 2004, Markov Chain Monte Carlo and Gibbs Sampling, Lecture Note for EEB 581, version (26) April 2004.
Casella, G., dan George, E. I., 1992, Explaining the Gibbs Sampler,The American Statistician, 46(3), 167-174.
Iriawan, N., 2000, Computationally Intensive Approaches to Inference in Neo-Normal Linier Models, DoctoralThesis, Australia, CUT.
Rasmussen, C.E., 2000, The Infinite Gaussian Mixture Model, Denmark, MIT Press.
Panik, M.J., 2005, Advanced Statistics from An Elementary Point of View, United States, Elsevier Academic Press.
Bolstad, W.M., 2007, Introduction to Bayesian Statistics 2ndEdition, Amerika, John Wiley & Sons. Inc.