Kolmogorov Smirnov test for normality of data was conducted (Table-II).
The
Kolmogorov model and Schneider rate equation were successfully applied to simulate the crystallization behavior of neat polymers under complex thermal and flow histories.
The
Kolmogorov Smirnov test detected data not normal of the performance coefficient of the 1st and 2nd set of all skills.
The solution of the
Kolmogorov forward equation is a probability density function that is non negative with constant integral over the domain.
Embedding into L(X) and the
Kolmogorov Superposition Theorem
By comparison with
Kolmogorov's test (see, e.g., [8, 4.12]), we do not need to assume that g(t)[??] [infinity] as t [right arrow] [infinity] in Theorem 2.1.
In 1941, Russian physicist Andrei
Kolmogorov worked out the spectrum of wind speed fluctuations.
Our main hypothesis is that algorithmic entropy, also known as
Kolmogorov complexity, is superior to traditional Shannon entropy due to the fact that algorithmic entropy is more robust, less dependent on the network representation, and better aligned with intuitive human understanding of complexity.
* [H.sub.0] hypothesis tested by
Kolmogorov adjustment.
Secondly, through the Kolmogorov-Smirnov test (
Kolmogorov, 1933; Smirnov, 1948) we tested the normality of distribution.