From Inference to Physics

TL;DR

This paper advances entropic dynamics by deriving the probability of system paths and showing that maximum probability paths align with Newtonian physics, linking probabilistic inference with classical laws.

Contribution

It develops the entropic dynamics framework by calculating path probabilities and demonstrating how classical Newtonian dynamics emerge from probabilistic principles.

Findings

Path probability distribution derived for arbitrary system trajectories.

Maximum probability paths reproduce Newtonian dynamics.

Framework links probabilistic inference to classical physics laws.

Abstract

Entropic dynamics, a program that aims at deriving the laws of physics from standard probabilistic and entropic rules for processing information, is developed further. We calculate the probability for an arbitrary path followed by a system as it moves from given initial to final states. For an appropriately chosen configuration space the path of maximum probability reproduces Newtonian dynamics.