• # DPpackage

• Referenced in 67 articles [sw10495]
• DPpackage includes models for marginal and conditional density estimation, receiver operating characteristic curve analysis, interval ... eliciting the precision parameter of the Dirichlet process prior, and a general purpose Metropolis sampling...
• # bivariate

• Referenced in 1 article [sw36728]
• bimodal) distributions, trivariate (normal and Dirichlet) distributions, bivariate kernel density estimates and bivariate empirical cumulative...
• # dirichletprocess

• Referenced in 3 articles [sw32614]
• Markov chain Monte Carlo sampling. Our Dirichlet process objects can act as building blocks ... statistical models including and not limited to: density estimation, clustering and prior distributions in hierarchical...
• # pyMEF

• Referenced in 2 articles [sw07455]
• means, often using expectation-maximization or kernel density estimation. In addition to these well-known ... promising stochastic modeling methods include Dirichlet process mixtures and $k$-maximum likelihood estimators. Most ... compute. On the other hand, kernel density estimation yields large models which are computationally cheap...
• # BNPmix

• Referenced in 2 articles [sw28208]
• process mixture models, and Griffiths-Milne Dependent Dirichlet process mixture models. Pitman-Yor process mixture ... flexible Bayesian nonparametric models to deal with density estimation. Estimation could be done via importance ... importance conditional sampler a GM-Dependent Dirichlet process mixture model...