  The foundations of Statistical Mechanics can be recovered almost in their
entirety from the Principle of Maximum Entropy. In this work we show that its
non-equilibrium generalization, the Principle of Maximum Caliber (Jaynes,
1980), when applied to the unknown trajectory followed by a particle, leads to
Newton's second law under two quite intuitive assumptions (the expected square
displacement in one step and the spatial probability distribution of the
particle are known at all times). Our derivation explicitly highlights the role
of mass as an emergent measure of the fluctuations in velocity (inertia) and
the origin of potential energy as a manifestation of spatial correlations.
According to our findings, the application of Newton's equations is not limited
to mechanical systems, and therefore could be used in modelling ecological,
financial and biological systems, among others.

Data Analysis, Statistics and Probability;Statistical Mechanics