Graduation Year
2025
Document Type
Dissertation
Degree
Ph.D.
Degree Name
Doctor of Philosophy (Ph.D.)
Degree Granting Department
Computer Science and Engineering
Major Professor
Hao Zheng, Ph.D.
Committee Member
Srinivas Katkoori, Ph.D.
Committee Member
Mehran Mozaffari Kermani, Ph.D.
Committee Member
Qing Lu, Ph.D.
Committee Member
Chris Myers, Ph.D.
Keywords
Importance Sampling, Infinite State, Probabilistic Model Checking, Rare Events, Stochastic Simulation
Abstract
This dissertation addresses the challenges of stochastic analysis of safety-critical systems with biological components, where unexpected behavior can lead to catastrophic events. Two fundamental challenges hinder the analysis of such systems: their typically large or infinite state spaces, and the extreme rarity of error states of interest. While Monte Carlo simulation can analyze biochemical systems without storing the state space, accurately estimating rare event probabilities becomes computationally prohibitive. Conversely, probabilistic model checking excels at analyzing extremely low probability events but becomes impractical for systems with large or infinite state spaces due to memory constraints.This work proposes two main contributions to address these distinct challenges. To handle models with infinite state spaces, a property-guided state-space truncation method is proposed that preserves states with high contribution to error probability while reducing the state space to a size amenable to model checking. To address the rare event problem, this dissertation evaluates variance reduction methods that improve Monte Carlo simulation performance in analyzing rare events, and introduces a reinforcement learning approach to automate the parameter selection process. The performance of these frameworks is validated through case studies of biochemical systems with infinite state spaces and rare events, demonstrating improvements in both computational efficiency and automation over existing analysis methods. The results show the effectiveness of the proposed approach in addressing the dual challenges of infinite state spaces and extremely rare probabilities in stochastic system analysis.
Scholar Commons Citation
Ahmadi, Mohammad, "Efficient Methods and Algorithms for Analyzing Stochastic Systems" (2025). USF Tampa Graduate Theses and Dissertations.
https://digitalcommons.usf.edu/etd/10838
