Default priors for density estimation with mixture models

Default priors for density estimation with mixture models

The infinite mixture of normals model has become a popular method for density estimation problems. This paper proposes an alternative hierarchical model that leads to hyperparameters that can be interpreted as the location, scale and smoothness of the density. The priors on other parts of the model have little effect on the density estimates and can be given default choices. Automatic Bayesian ...

Density Estimation by Mixture Models with Smoothing Priors

In the statistical approach for self-organizing maps (SOMs), learning is regarded as an estimation algorithm for a gaussian mixture model with a gaussian smoothing prior on the centroid parameters. The values of the hyperparameters and the topological structure are selected on the basis of a statistical principle. However, since the component selection probabilities are fixed to a common value,...

Default Priors, Model Selection and Adaptation in Density Estimation

Default priors for density estimation may be constructed through certain approximating sieves with certain natural default priors on these sieves. Finite sieves with metric approximation properties and convolution-sieves provide natural examples and lead to consistent posteriors. Best rate of convergence may be achieved with more reened constructions of sieves using bracketing or spline functio...

Hierarchical priors and mixture models, with applications in regression and density estimation

Location Reparameterization and Default Priors for Statistical Analysis

This paper develops default priors for Bayesian analysis that reproduce familiar frequentist and Bayesian analyses for models that are exponential or location. For the vector parameter case there is an information adjustment that avoids the Bayesian marginalization paradoxes and properly targets the prior on the parameter of interest thus adjusting for any complicating nonlinearity the details ...