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Statistical Mechanics, which is due primarily to Maxwell, Gibbs, and Boltzmann in the ninetieth century, has
proven to be useful model for drawing inferences about the collective behavior of individual objects that interact
according to a known force law (which for more general usage is referred to as interacting units.). Collective
behavior is determined not by computing F = ma for each interacting unit because the problem is mathematically
intractable. Instead, one computes the partition function for the collection of interacting units and predicts
statistical behavior from the partition function. Statistical mechanics was united with Bayesian inference by
Jaynes [4]. As a continuation, Shannon [7] demonstrated that the partition function assignment of probabilities
via the interaction Hamiltonian is the solution to Bayesian assignment of probabilities (based on the maximum
entropy method with known means and standard deviations). Once this technique has been applied to a variety
of problems and obtained a solution, one can, of course, solve the inverse problem of to determine the solution to
an inverse problem to determine what interaction model gives rise to a given probability assignment [1] and [8].
The usage of statistical mechanics allows one can draw general inferences about any complex system including
networks [5] by defining "energy", "heat capacity", "temperature", and other thermodynamic characteristics
of most complex systems based on the common standard of the Helmholtz free energy. Principle has noted
that the aspect of entropy used in reasoning with uncertainty may not be the most appropriate entropy for
learning mechanisms [6]. Instead he has explored using Renyi entropy and derived a form of information learning
dynamics that has some promising features [2]. To fully realize the potential of the usage of a more generalized
entropy to the three aspects of survival, we suggest some connections to the free energy and learning. We also
connect some aspects of sensing to probability distributions that suggest why certain search strategies perform
better than others. In making these connections, we suggest a fundamental connection waits to be discovered
between inference, learning, and related to the manner in which sensing mechanisms perform.
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