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September 5, 2025

Stochastic Process Inference Without Trajectories: A Probabilistic Approach

Key Points

The approach resolves the challenge of inferring dynamics without direct trajectory data, crucial for many systems.
Utilizing observations, the methodology estimates stochastic equations governing system behavior under uncertainty.
This method addresses a significant gap in stochastic process analysis, particularly in complex systems and natural sciences.
The findings may lead to advancements in understanding system performance across various applications and sectors.

Abstract

A fundamental problem in computer system performance, as well as in the natural sciences, concerns inferring from observations an understanding of the behavior of stochastic processes of interacting system components whose dynamics are driven by an unknown underlying stochastic differential equation (SDE). The objective in solving this problem is to infer the underlying equations of the dynamics of the system from sets of system measurements, indexed over time. Given the stochastic nature of such systems, together with a lack of information on stochastic trajectories in many cases 1, 3, this represents a very challenging problem in general.

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