ArticleIEEE transactions on artificial intelligence2024
Optimal Inference of Hidden Markov Models Through Expert-Acquired Data.
Article in IEEE transactions on artificial intelligence, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Deep Reinforcement Learning for Intervention of Partially Observable Regulatory Networks.Proceedings of the ... American Control Conference. American Control Conference · 2025Article
- Pareto-Optimal Interventions in Gene Regulatory Networks using Signal Temporal Logic.Proceedings of the ... American Control Conference. American Control Conference · 2025Article
- Bayesian Topology Inference of Regulatory Networks under Partial Observability.Results in control and optimization · 2025Article
- Deep Reinforcement Learning Data Collection for Bayesian Inference of Hidden Markov Models.IEEE transactions on artificial intelligence · 2025Article
- Dynamic Intervention in Gene Regulatory Networks: A Partially Observed Zero-Sum Markov Game.Control Technology and Applications. Control Technology and Applications · 2024Article
- Bayesian Optimization for State and Parameter Estimation of Dynamic Networks with Binary Space.Control Technology and Applications. Control Technology and Applications · 2024Article
- Bayesian reinforcement learning for navigation planning in unknown environments.Frontiers in artificial intelligence · 2024Article
- Kernel-Based Particle Filtering for Scalable Inference in Partially Observed Boolean Dynamical Systems.IFAC-PapersOnLine · 2024Article
- Deep Reinforcement Learning Sensor Scheduling for Effective Monitoring of Dynamical Systems.Systems science & control engineering · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
This paper focuses on inferring a general class of hidden Markov models (HMMs) using data acquired from experts. Expert-acquired data contain decisions/actions made by humans/users for various objectives, such as navigation data reflecting drivers' behavior, cybersecurity data carrying defender decisions, and biological data containing the biologist's actions (e.g., interventions and experiments). Conventional inference methods rely on temporal changes in data without accounting for expert knowledge. This paper incorporates expert knowledge into the inference of HMMs by modeling expert behavior as an imperfect reinforcement learning agent. The proposed method optimally quantifies experts' perceptions about the system model, which, alongside the temporal changes in data, contributes to the inference process. The proposed inference method is derived through a combination of dynamic programming and optimal recursive Bayesian estimation. The applicability of this method is demonstrated to a wide range of inference criteria, such as maximum likelihood and maximum a posteriori. The performance of the proposed method is investigated through a comprehensive numerical experiment using a benchmark problem and biological networks.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.