References

Bishop, C. (2006). Pattern Recognition and Machine Learning. First Edition (Springer New York, NY).

Blei, D. M. and Jordan, M. I. (2006). Variational inference for Dirichlet process mixtures. Bayesian Analysis 1, 121–143.

Frühwirth-Schnatter, S.; Malsiner-Walli, G. and Grün, B. (2021). Generalized mixtures of finite mixtures and telescoping sampling. Bayesian Analysis 16, 1279–1307.

Gelman, A.; Carlin, J.; Stern, H.; Dunson, D.; Vehtari, A. and Rubin, D. (2013). Bayesian Data Analysis. Third Edition (Chapman and Hall/CRC).

Hastie, T.; Tibshirani, R. and Friedman, J. (2009). The elements of statistical learning. Second Edition (Springer Verlag).

Ishwaran, H. and James, L. (2001). Gibbs sampling methods for stick-breaking priors. Journal of the American statistical Association 96, 161–173.

Neal, R. M. (2000). Markov chain sampling methods for Dirichlet process mixture models. Journal of Computational and Graphical Statistics 9, 249–265.

Ormerod, J. and Wand, M. (2010). Explaining variational approximations. The American Statistician 64, 140–153.

Petrone, S. (1999). Bayesian density estimation using Bernstein polynomials. Canadian Journal of Statistics 27, 105–126.

Richardson, S. and Green, P. (1997). On Bayesian analysis of mixtures with an unknown number of components. Journal of the Royal Statistical Society Series B: Statistical Methodology 59.

Scott, D. (1992). Multivariate Density Estimation: Theory, Practice, and Visualization (Wiley).

Wand, M. and Yu, J. (2022). Density estimation via Bayesian inference engines. AStA Advances in Statistical Analysis 106, 199–216.

Wand, M. P. and Ormerod, J. T. (2008). On semiparametric regression with O'Sullivan penalized splines. Australian & New Zealand Journal of Statistics 50, 179–198.

Watanabe, S. (2010). Asymptotic equivalence of Bayes cross validation and widely applicable information criterion in singular learning theory. Journal of Machine Learning Research 11, 3571–3594.