# GPS Fit Method for Paths of Non Drunken Sailors and its Connection to Entropy

## TL;DR

This paper introduces a GPS path estimation method for cyclists based on shortest path assumptions, connecting entropy and likelihood through an entropic force, without requiring smoothing parameters.

## Contribution

It presents a novel shortest path-based algorithm for GPS data analysis that links entropy concepts to likelihood estimation, eliminating the need for smoothing parameters.

## Key findings

- The method accurately estimates cyclist altimeter climbs from noisy GPS data.
- A new connection between entropy and likelihood is established via an entropic force.
- The approach simplifies GPS path analysis by removing smoothing parameter dependencies.

## Abstract

Estimating the altimeters a cyclist has climbed from noisy GPS data is a challenging problem. In this article a method is proposed that assumes that a person locally takes the shortest path. This results in an algorithm that does not need smoothing parameters. Moreover, it turns out that this assumption allows one to find a similarity between entropy and likelihood which results to the introduction of an entropic force.