Data Analysis in Astronomy by Mike Disney (auth.), V. Di Gesù, L. Scarsi, P. Crane, J. H.

By Mike Disney (auth.), V. Di Gesù, L. Scarsi, P. Crane, J. H. Friedman, S. Levialdi (eds.)

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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.

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