As studied in Section 'Disparity vs. depth', since the n data points (landmarks) do not show a linear distribution; therefore, we must use nonlinear fitting models to approximate ∆.
Thus, as we discuss later in this section, the extraordinary disparities in life expectancy, child survival and health that distinguish those who live in rich and poor countries constitute a profound injustice that it is the duty of the global community to redress.
This section regarding racial/ethnic disparities addresses access to health services by focusing on health insurance, dental insurance, and school health services.
In "Sparse model for disparity" section, we discuss the sparse autoencoder for learning and inferring the sparse representation of disparities and then discuss the formation of sparsity prior.
In the previous section, the largest disparity between respondents occurred for foreigners skilled only in English.
The IGMRF prior model and estimation of IGMRF parameters are addressed in the "IGMRF model for disparity" section.
This section first presents the disparities that exist in certain sociodemographic characteristics, including racial, ethnic, and socioeconomic variables, between those communities within a 50-mile radius from a NPP (host communities) and those communities outside of a 50-mile radius, based on U.S. Census data for the years 1990, 2000, and 2010.
The d v E (estimated 95 percentile disparities, see Section 3.3) scores of the CGO algorithm on the whole set of video data (12 scenes resulting in 9,963 frames) are plotted in Figure 6 together with the number of subjects reporting eye strain for each scene.
However, in this section, we address the disparity issue.
All of the presented disparity maps (in Figures 11, 12 and 13) have been post-processed with confidence checking and speckle removal (Section 2.5) to eliminate disparity noise.
