Bayesian reconstruction based on flexible prior models
نویسنده
چکیده
A new approach to Bayesian reconstruction is proposed that endows the prior probability distribution with an inherent geometrical flexibility, which is achieved through a transformation of the coordinate system of the prior distribution or model into that of the reconstruction. With this warping, prior morphological information regarding the object that is being reconstructed may be adapted to various degrees to match the available measurements. The extent of warping is readily controlled through the prior probability distributions that are specified for the warp parameters. The complete reconstruction consists of a warped version of the prior model plus an estimated deviation from the warped model. Examples of tomographic reconstructions demonstrate the power of this approach.
منابع مشابه
Reconstruction based on flexible prior models
A new approach to Bayesian reconstruction is introduced in which the prior probability distribution is endowed with an inherent geometrical flexibility. This flexibility is achieved through a warping of the coordinate system of the prior distribution into that of the reconstruction. This warping allows various degrees of mismatch between the assumed prior distribution and the actual distributio...
متن کاملIntroducing of Dirichlet process prior in the Nonparametric Bayesian models frame work
Statistical models are utilized to learn about the mechanism that the data are generating from it. Often it is assumed that the random variables y_i,i=1,…,n ,are samples from the probability distribution F which is belong to a parametric distributions class. However, in practice, a parametric model may be inappropriate to describe the data. In this settings, the parametric assumption could be r...
متن کاملFlexible 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...
متن کاملLocation 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 ...
متن کاملTomographic Reconstruction Based on Flexible Geometric Models
When dealing with ill-posed inverse problems in data analysis, the Bayesian approach allows one to use prior information to guide the result toward reasonable solutions. In this work the model consists of an object whose amplitude is constant inside a flexible boundary. The flexibility of the boundary is controlled by through a distortion energy. We present an example of reconstruction of the c...
متن کامل