Classical statistics would also equally account for repeated throws of this single dice.
2.
Classical statistics would then be able predict what typically would be the number of times that certain results would occur.
3.
It is frequently used in Bayesian statistics, empirical Bayes methods and classical statistics to capture overdispersion in binomial type distributed data.
4.
In classical statistics, sum-minimization problems arise in least squares and in maximum-likelihood estimation ( for independent observations ).
5.
However, classical statistics would not be able to predict what definite single result would occur with a single throw of the pair of dice.
6.
That's the difference between being indistinguishable ( Bose-Einstein statistics ) and merely having some identical properties ( " classical statistics " ).
7.
Thus, the maximum entropy principle is not merely an alternative way to view the usual methods of inference of classical statistics, but represents a significant conceptual generalization of those methods.
8.
Bandyopadhyay & Forster describe four paradigms : " ( i ) classical statistics or error statistics, ( ii ) Bayesian statistics, ( iii ) likelihood-based statistics, and ( iv ) the Akaikean-Information Criterion-based statistics ".
9.
Bandyopadhyay & Forster describe four statistical paradigms : " ( 1 ) classical statistics or error statistics, ( ii ) Bayesian statistics, ( iii ) likelihood-based statistics, and ( iv ) the Akaikean-Information Criterion-based statistics ".
10.
In related work, he has argued that classical particles could be treated as indistinguishable in exactly the same way that quantum particles ( and that departures from classical statistics can be traced to discrete nature of the measure-- dimensionality of subspace of Hilbert space ), and applied this to the Gibbs paradox.
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