Order-invariant prior specification in Bayesian factor analysis

Bayesian CART – Prior Specification and Posterior Simulation –

We present advances in Bayesian modeling and computation for CART (classification and regression tree) models. The modeling innovations include a formal prior distributional structure for tree generation – the pinball prior – that allows for the combination of an explicit specification of a distribution for both the tree size and the tree shape. The core computational innovations involve a nove...

Normalized Power Prior Bayesian Analysis Normalized Power Prior Bayesian Analysis

High-Order MRF Prior Based Bayesian Deblurring

A novel image deblurring method based on high-order non-local range Markov Random Field (NLR-MRF) prior is proposed in the paper. NLR-MRF provides an effective framework to model the statistical prior of natural images and leads to excellent performance in the application of image denoising and inpainting. Moreover, the framework will be extended to image deblurring in our work. Instead of comm...

Flexible Prior Models in Bayesian Image Analysis

A new class of prior models is proposed for Bayesian image analysis. This class of priors provides an inherent geometrical flexibility, which is achieved through a transformation of the coordinate system of the prior distribution or model into that of the object under analysis. Thus prior morphological information about the object being reconstructed may be adapted to various degrees to match t...

Default Prior Distributions and Efficient Posterior Computation in Bayesian Factor Analysis.

Factor analytic models are widely used in social sciences. These models have also proven useful for sparse modeling of the covariance structure in multidimensional data. Normal prior distributions for factor loadings and inverse gamma prior distributions for residual variances are a popular choice because of their conditionally conjugate form. However, such prior distributions require elicitati...