참고문헌
- Antoniak, C. E. (1974). Mixtures of Dirichlet processes with applications to Bayesian nonparametric problems, The Annals of Statistics, 2, 1152-1174. https://doi.org/10.1214/aos/1176342871
- Baek, S., Jeong, S., Choi, J. S., and Lee, S. (2015). Effective noise reduction using STFT-based content analysis, Journal of The Institute of Electronics and Information Engineers, 52, 145-155. https://doi.org/10.5573/ieie.2015.52.4.145
- Escobar, M. D. (1994). Estimating normal means with a Dirichlet process prior, Journal of the American Statistical Association, 89, 268-277. https://doi.org/10.1080/01621459.1994.10476468
- Escobar, M. D. and West, M. (1995). Bayesian density estimation and inference using mixtures, Journal of the American Statistical Association, 90, 577-588. https://doi.org/10.1080/01621459.1995.10476550
- Escobar, M. D. and West, M. (1998). Computing nonparametric hierarchical models. In Practical Non-parametric and Semiparametric Bayesian Statistics (pp. 1-22). Springer, New York.
- Ferguson, T. S. (1973). A Bayesian analysis of some nonparametric problems, The Annals of Statistics, 1, 209-230. https://doi.org/10.1214/aos/1176342360
- Ferguson, T. S. (1983). Bayesian density estimation by mixtures of normal distributions. In Recent Advances in Statistics, Academic Press.
- Gonzalez, R. C. and Woods, R. E. (2008). Digital Image Processing (3rd ed), Prentice Hall, NJ.
- Griffin, J. E. (2010). Default priors for density estimation with mixture models, Bayesian Analysis, 5, 45-64. https://doi.org/10.1214/10-BA502
- Held, L. and Bove, D. S. (2014). Bayesian Inference. In Applied Statistical Inference, Springer, Berlin, Heidelberg.
- Ishwaran, H. and James, L. F. (2002). Approximate Dirichlet process computing in finite normal mixtures: smoothing and prior information, Journal of Computational and Graphical Statistics, 11, 508-532. https://doi.org/10.1198/106186002411
- Jara, A., Hanson, T., Quintana, F., Mueller, P., Rosner, G., and Jara, M. A. (2018). Package 'DPpackage'.
- Kim, Y. H. and Nam, J. (2011). Estimation of the noise variance in image and noise reduction, The Korean Journal of Applied Statistics, 24, 905-914. https://doi.org/10.5351/KJAS.2011.24.5.905
- Kim, Y. H. (2012). Adaptive noise reduction algorithm for image based on block approach, Communications for Statistical Applications and Methods, 19, 225-235. https://doi.org/10.5351/CKSS.2012.19.2.225
- Lindsay, B. G. (1983). The geometry of mixture likelihoods: a general theory, The Annals of Statistics, 11, 86-94. https://doi.org/10.1214/aos/1176346059
- Muller, P., Erkanli, A., and West, M. (1996). Bayesian curve fitting using multivariate normal mixtures, Biometrika, 83, 67-79. https://doi.org/10.1093/biomet/83.1.67
- Muller, P. and Quintana, F. A. (2004). Nonparametric Bayesian data analysis, Statistical Science, 19, 95-110. https://doi.org/10.1214/088342304000000017
- Neal, R. M. (2000). Markov chain sampling methods for Dirichlet process mixture models, Journal of Computational and Graphical Statistics, 9, 249-265.
- Ohlssen, D. I., Sharples, L. D., and Spiegelhalter, D. J. (2007). Flexible random-effects models using Bayesian semi-parametric models: applications to institutional comparisons, Statistics in Medicine, 26, 2088-2112. https://doi.org/10.1002/sim.2666
- Silverman, B. W. (1986). Density Estimation for Statistics and Data Analysis, Chapman and Hall, London.
- Song, M., Park, C., and Lee, J. (2003). Non-Parametric Statistics using S-LINK, Free Academy, Seoul.
- Tierney, L. (1994). Markov chains for exploring posterior distributions, The Annals of Statistics, 22, 1701-1728. https://doi.org/10.1214/aos/1176325750
- West, M. (1990). Bayesian kernel density estimation, Institute of Statistics and Decision Sciences, Duke University.
- West, M. (1992). Hyperparameter estimation in Dirichlet process mixture models. ISDS Discussion Paper# 92-A03: Duke University.
- West, M. and Cao, G. (1993). Assessing mechanisms of neural synaptic activity. In Case Studies in Bayesian Statistics. Springer, New York.
- Zhou, M., Chen, H., Paisley, J., et al. (2012). Nonparametric Bayesian dictionary learning for analysis of noisy and incomplete images, IEEE Transactions on Image Processing, 21, 130-144. https://doi.org/10.1109/TIP.2011.2160072