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.

Corradin, R.; Canale, A. and Nipoti, B. (2021). BNPmix: an R package for Bayesian nonparametric modeling via Pitman–Yor mixtures. Journal of Statistical Software 100, 1–33.

De Blasi, P.; Favaro, S.; Lijoi, A.; Mena, R. H.; Prünster, I. and Ruggiero, M. (2015). Are Gibbs-type priors the most natural generalization of the Dirichlet process? IEEE Transactions on Pattern Analysis and Machine Intelligence 37, 212–229.

Frühwirth-Schnatter, S. (2006). Finite Mixture and Markov Switching Models (Springer).

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).

He, V. X. and Wand, M. P. (2025), gamselBayes: Bayesian Generalized Additive Model Selection. R package version 2.0-3.

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.

Petrone, S. and Wasserman, L. (2002). Consistency of Bernstein polynomial posteriors. Journal of the Royal Statistical Society Series B: Statistical Methodology 64, 79–100.

Pham, T. H. and Wand, M. P. (2018). Generalised additive mixed models analysis via gammSlice. Australian & New Zealand Journal of Statistics 60, 279–300.

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.

Rousseau, J. and Mengersen, K. (2011). Asymptotic behaviour of the posterior distribution in overfitted mixture models. Journal of the Royal Statistical Society Series B: Statistical Methodology 73, 689–710.

Roy, V. (2020). Convergence diagnostics for Markov Chain Monte Carlo. Annual Review of Statistics and Its Application 7, 387–412.

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

Vehtari, A.; Gelman, A.; Simpson, D.; Carpenter, B. and Bürkner, P.-C. (2021). Rank-normalization, folding, and localization: An improved R hat ̂for assessing convergence of MCMC (with discussion). Bayesian analysis 16, 667–718.

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

Wand, M. P. (2023), densEstBayes: Density Estimation via Bayesian Inference Engines. R package version 1.0-2.2.

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.