Development and Interpretation of a Deep Learning Algorithm for Multi-State Model Analysis

Abstract

This research presents the development of a deep learning algorithm grounded in hidden variable models for the analysis of multi-state systems. Accurately estimating the joint probability distribution of interrelated variables remains a significant challenge, particularly in scenarios where the state data is either unavailable or only weakly linked to the observed correlation data. Hidden variable models address this problem by introducing latent or unobserved factors that can effectively capture and explain the variations in the observed data. System identification, which focuses on developing mathematical models of dynamic systems based on observed input-output data, shows a natural synergy with machine learning methodologies. By leveraging this connection, we propose structural simplifications to hidden variable models that aim to improve their capacity to represent critical state variables and enhance their performance in system identification applications. These simplifications make the models more interpretable and computationally tractable, while still preserving the essential dynamics of multi-state systems, thereby improving the reliability and practicality of their deployment.