Journal of the American Statistical Association, December 1st, 1995
This book shows how probability and statistical inference can be defined in terms of a Hilbert space by replacing the classical definition using sample space with an equivalent Hilbert space. The major motivation for this replacement approach is to enhance the interpretation of many probability and statistical inference tools. For example, data reduction due to information loss or sufficiency are simple projections in the Hilbert space approach. This gives us new geometric interpretations to classical terms in probability and statistical inference.
The preface motivates and justifies the us...
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