![]() ![]() It will be shown that Bayesian probability theory solves all of the problems with the maximum entropy method of moments. Then in later sections, the functional form of the maximum entropy method of moments probability distribution will be incorporated into Bayesian probability theory. Some of its problems and conditions under which it fails will be discussed. In the introduction, the maximum entropy method of moments will be reviewed. Three common techniques for estimating density functions are binned histograms, kernel density estimation, and the maximum entropy method of moments. However, in the Non-Gaussian case the shape of the density function itself must be inferred. In the case of a Gaussian distribution, one must estimate the mean and standard deviation. Either way, estimating the distribution of intensities is an inference problem. Often this distributions can be characterized by a Gaussian, but just as often it is much more complicated. ![]() In a given tissue type there is no single intensity that characterizes the tissue, rather there is a distribution of intensities. These characteristics might be the intensity of a T1 weighted image and in MRI many other types of characteristic weightings (classifiers) may be generated. To perform this classification one must first identify the characteristics of the tissues to be classified. For example, in MRI it is often necessary to classify the types of tissues in an image. The problem of density estimation occurs in many disciplines. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |