Markov Decision Processes with Their Applications

Markov Decision Processes With Their App

📁 Markov Decision Processes With Their Applications

✍ Professor Qiying Hu, Professor Wuyi Yue (auth.) 📂 Library 📅 2008 🏛 Springer US 🌐 English

Markov decision processes (MDPs), also called stochastic dynamic programming, were first studied in the 1960s. MDPs can be used to model and solve dynamic decision-making problems that are multi-period and occur in stochastic circumstances. There are three basic branches in MDPs: discrete-time

Markov Decision Processes With Their App

📁 Markov Decision Processes With Their Applications

✍ Professor Qiying Hu, Professor Wuyi Yue (auth.) 📂 Library 📅 2008 🏛 Springer US 🌐 English

Markov decision processes (MDPs), also called stochastic dynamic programming, were first studied in the 1960s. MDPs can be used to model and solve dynamic decision-making problems that are multi-period and occur in stochastic circumstances. There are three basic branches in MDPs: discrete-time

Markov Decision Processes with Their App

📁 Markov Decision Processes with Their Applications (Advances in Mechanics and Mathematics)

✍ Qiying Hu, Wuyi Yue 📂 Library 📅 2007 🌐 English

Markov Decision Processes with Applicati

📁 Markov Decision Processes with Applications to Finance

✍ Nicole Bäuerle, Ulrich Rieder (auth.) 📂 Library 📅 2011 🏛 Springer-Verlag Berlin Heidelberg 🌐 English

The theory of Markov decision processes focuses on controlled Markov chains in discrete time. The authors establish the theory for general state and action spaces and at the same time show its application by means of numerous examples, mostly taken from the fields of finance and operations res

Markov decision processes with applicati

📁 Markov decision processes with applications to finance

✍ Nicole Bäuerle, Ulrich Rieder (auth.) 📂 Library 📅 2011 🏛 Springer-Verlag Berlin Heidelberg 🌐 English

The theory of Markov decision processes focuses on controlled Markov chains in discrete time. The authors establish the theory for general state and action spaces and at the same time show its application by means of numerous examples, mostly taken from the fields of finance and operations res

Markov Point Processes and Their Applica

📁 Markov Point Processes and Their Applications

✍ M N M Van Lieshout 📂 Library 📅 2000 🏛 Imperial College Press 🌐 English

The splitting extrapolation method is a newly developed technique for solving multidimensional mathematical problems. It overcomes the difficulties arising from Richardson's extrapolation when applied to these problems and obtains higher accuracy solutions with lower cost and a high degree of parall

Markov Decision Processes with Their Applications

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