Posterior Sampling From Truncated Ferguson-Klass Representation of Normalised Completely Random Measure Mixtures
Author:
Affiliation:
1. Department of Applied Mathematics, The Hong Kong Polytechnic University, Kowloon, Hong Kong
2. Department of Statistics, London School of Economics, London WC2A 2AE, UK
Publisher
Institute of Mathematical Statistics
Reference62 articles.
1. Ghosal, S. and van der Vaart, A. (2017). Fundamentals of Nonparametric Bayesian Inference, volume 44 of Cambridge Series in Statistical and Probabilistic Mathematics. Cambridge University Press, Cambridge.
2. Campbell, T., Huggins, J. H., How, J. P., and Broderick, T. (2019). “Truncated random measures.” Bernoulli, 25(2): 1256–1288.
3. Regazzini, E., Lijoi, A., and Prünster, I. (2003). “Distributional results for means of normalized random measures with independent increments.” The Annals of Statistics, 31(2): 560–585.
4. Herlau, T., Schmidt, M. N., and Mørup, M. (2016). “Completely random measures for modelling block-structured sparse networks.” In Lee, D., Sugiyama, M., Luxburg, U., Guyon, I., and Garnett, R. (eds.), Advances in Neural Information Processing Systems, volume 29. Curran Associates, Inc.
5. Haario, H., Saksman, E., and Tamminen, J. (2001). “An adaptive Metropolis algorithm.” Bernoulli, 7(2): 223–242.
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