PROJECT TITLE :

Bayesian Estimation in the Presence of Deterministic Nuisance Parameters—Part II: Estimation Methods

ABSTRACT:

One in all the elemental issues of estimation theory is the presence of deterministic nuisance parameters. Whereas within the Bayesian paradigm the model parameters are random, introduction of deterministic nuisance parameters into the model exceeds the Bayesian framework to the hybrid framework. During this type of eventualities, the conventional Bayesian estimators don't seem to be valid, as they assume data of the deterministic nuisance parameters. This paper is the second of a 2-half study of Bayesian parameter estimation in the presence of deterministic nuisance parameters. In part I, a replacement Cramér–Rao (CR)-type bound on the mean-square-error (MSE) for Bayesian estimation in the presence of deterministic nuisance parameters was established based mostly on the concept of risk-unbiasedness. The proposed certain was named risk-unbiased bound (RUB). This paper presents properties of asymptotic uniform mean- and risk-unbiasedness of some Bayesian estimators: 1) the minimum MSE (MMSE) or most a posteriori probability (MAP) estimators with maximum chance (ML) estimates substituting the deterministic parameters, named MS-ML and MAP-ML, respectively, and 2) joint MAP and ML estimator, named JMAP-ML. Furthermore, an asymptotic performance analysis of the MS-ML and MAP-ML estimators is presented. These estimators are shown to asymptotically achieve the RUB, while the existing CR-type bounds can be achieved solely in distinct cases. Simulations verify these results for the problem of blind separation of nonstationary sources. It is shown that not like existing CR-kind bounds, the RUB is asymptotically tight.


Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here


PROJECT TITLE : Robust Empirical Bayesian Reconstruction of Distributed Sources for Electromagnetic Brain Imaging ABSTRACT: Electromagnetic brain imaging uses non-invasive recordings of magnetic fields and electric potentials
PROJECT TITLE : Variational Bayesian Blind Color Deconvolution of Histopathological Images ABSTRACT: In most whole-slide histology images, two or more chemical dyes are used. In digital pathology, slide stain separation or colour
PROJECT TITLE : Bayesian Polytrees With Learned Deep Features for Multi-Class Cell Segmentation ABSTRACT: Quantitative cell biology relies heavily on being able to identify different cell compartments, cell types, and the ways
PROJECT TITLE : Generalized Bayesian Model Selection for Speckle on Remote Sensing Images ABSTRACT: Coherent summation of back-scattered waves and subsequent nonlinear envelope changes introduce speckle noise into both synthetic
PROJECT TITLE : High Quality Bayesian Pansharpening ABSTRACT: Fusion of low-resolution multispectral images with high resolution panchromatic images is known as pansharpening, and the result is a multi-spectral image with high

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry