Organodynamics	Grant Holland, Apr 25, 2014
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13. Organodynamic Dependent Stochastic Processes Organodynamics leverages these entropic functionals of information theory to characterize the time evolution of OSPs. To do this, organodynamics defines a more elaborate type of OSP – the organodynamic dependent stochastic process (ODSP) that uses Markov chains and their generalizations. Since the ODSP represents joint distributions, the subject of stochastic dependence/independence is now in play, and the entropic functionals that represent stochastic dependence are leveraged to model the dynamics of this class of complex systems.
14. Mathematical Statistics or Information Theory? Mathematical statistics, like information theory, is a branch of probability theory that characterizes chance variation and its degree within a probability space. Where information theory uses the “toolset” called entropic functionals to characterize chance variation, mathematical statistics emphasizes central moments (mean, variance, skewness, etc.). Both of these “repertoires” have their advantages and disadvantages.	However, the central moments are not generally defined for organodynamics! Thus, information theory is the only branch of probability theory that is available for organodynamics.

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