Corpus ID: 2533797

Hyperparameter estimation in Dirichlet process mixture models

@inproceedings{West1992HyperparameterEI,
  title={Hyperparameter estimation in Dirichlet process mixture models},
  author={B. West},
  year={1992}
}
  • B. West
  • Published 1992
  • Mathematics
  • In Bayesian density estimation and prediction using Dirichlet process mixtures of standard, exponential family distributions, the precision or total mass parameter of the mixing Dirichlet process is a critical hyperparameter that strongly influences resulting inferences about numbers of mixture components. This note shows how, with respect to a flexible class of prior distributions for this parameter, the posterior may be represented in a simple conditional form that is easily simulated. As a… CONTINUE READING
    126 Citations
    Bayesian Density Estimation and Inference Using Mixtures
    • 2,153
    • PDF
    Bayesian density regression
    • 224
    • PDF
    On selecting the hyperparameters of the DPM models for the density estimation of observation errors
    • 7
    • Highly Influenced
    • PDF
    Bayesian Inference for Linear Dynamic Models With Dirichlet Process Mixtures
    • 92
    • PDF
    Estimating Mixture of Dirichlet Process
    • ModelsSteven N. MacEachern
    • 2006
    • 5
    • PDF
    Computing Nonparametric Hierarchical Models
    • 129
    Mixture Models With a Prior on the Number of Components
    • 89
    • PDF
    A permutation-augmented sampler for DP mixture models
    • 19
    • Highly Influenced
    • PDF

    References

    SHOWING 1-4 OF 4 REFERENCES
    Mixtures of Dirichlet Processes with Applications to Bayesian Nonparametric Problems
    • 1,926
    • Highly Influential
    • PDF
    Sampling-Based Approaches to Calculating Marginal Densities
    • 6,666
    • PDF