Suppose we'd like to visualize a set of joint probabilities realizable by distributions of 3 binary variables.
It is a 7 dimensional regular simplex, and we could draw a lower dimensional section of it. There's an infinite number of sections, but there aren't that many *interesting* ones.
As an example, suppose we want to find a good 2 dimensional section of a 3 dimensional regular simplex. It seems reasonable to restrict attention to sections that go through simplex center and two other points, each of which is a vertex or a center of some edge or face. There are just 2 such sections determined up to rotation/reflection
For 7 dimensional simplex, we could similarly take 3 dimensional sections determined by simplex center and 3 points, each a centroid of some set of vertices. This gives 49 distinct shapes, shown below.
Among those, there's just one section that intersects all 8 faces of the simplex and preserves the underlying symmetry
Also known as the regular octahedron.
Implementation details
5 comments:
THANKS FOR THE INFORMATION...
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Excellent machine learning blog,thanks for sharing...
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I wonder if we may share this with Alan Wallace and Lama Padma Samten
Thanks for the information... I really love your blog posts..
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