Partially Observed Markov Decision Processes: Filtering, Learning and Controlled Sensing
Partially Observed Markov Decision Processes: Filtering, Learning and Controlled Sensing presents a focused treatment of decision-making when system information is incomplete. Centered on filtering, learning, and controlled sensing, this academic work explores the core concepts and methods associated with partially observed Markov decision processes.
About This Book
Partially Observed Markov Decision Processes: Filtering, Learning and Controlled Sensing focuses on a mathematical and computational topic in decision-making under uncertainty.
The book addresses filtering, learning, and controlled sensing within the framework of partially observed Markov decision processes.
It is intended for readers seeking a focused treatment of these interconnected subjects.
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