By Mike Disney (auth.), V. Di Gesù, L. Scarsi, P. Crane, J. H. Friedman, S. Levialdi (eds.)
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Extra info for Data Analysis in Astronomy
We are, of course, not restricted to a single low-dimensional view. Models of a response will usually be constructed from several views. Interactive versions of the automatic projection pursuit methods are being developed for Orion I. The system allows a user to manually imitate the Rosenbrock search strategy [111 used by the numerical optimizer in the automatic versions. However, a human being can search to optimize subjective criteria, using perception and judgment, instead of having to rely on a single, numerical measure of what constitutes an interesting view.
Higher dimensional views: we try to represent as many variables as pos- sible in a single picture. 2. Projection Pursuit: we try to find a low dimensional picture that captures the structure in the many dimensional data space. 3. Multiple Views: we look at several low dimensional views simultaneously; by making connections between the low dimensional views, we hope to see higher dimensional structure. Higher Dimensional Views One way to see high-dimensional structure is to try to invent pictures 49 that show as many dimensions at a time as possible.
How we analyze a given data set is very dependent on context. Sometimes we are given data that arises from an experiment designed to answer a particular question. In this case, we may have a great deal of prior knowledge about our data, and have confidence that we know what to expect in it. This is a situation in which statistical inference may be appropriate. An inference is a generalization from a given data set to some larger population (real or hypothetical) from which the data set is presumed to be a sample.