Bayesian Networks and Decision Graphs
Probabilistic graphical models and decision graphs are powerful modeling tools for reasoning and decision making under uncertainty. ...
Weiterlesen
Bayesian Networks and Decision Graphs
Probabilistic graphical models and decision graphs are powerful modeling tools for reasoning and decision making under uncertainty. ...
WeiterlesenDistributed Artificial Intelligence : Second International Conference, DAI 2020, Nanjing, China, October 24–27, 2020, Proceedings
This book constitutes the refereed proceedings of the Second International Conference on Distributed Artificial Intelligence, ...
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Formal Methods and Testing : An Outcome of the FORTEST Network, Revised Selected Papers
This book constitutes the thoroughly refereed and peer-reviewed outcome of the Formal Methods and Testing (FORTEST) network ...
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Machine Learning: ECML 2005
The European Conference on Machine Learning (ECML) and the European Conference on Principles and Practice of Knowledge Discovery ...
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Markov Chains: Models, Algorithms and Applications
Markov chains are a particularly powerful and widely used tool for analyzing a variety of stochastic (probabilistic) systems ...
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Markov Chains: Models, Algorithms and Applications
Markov chains are a particularly powerful and widely used tool for analyzing a variety of stochastic (probabilistic) systems ...
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Markov Decision Processes with Their Applications
Markov decision processes (MDPs), also called stochastic dynamic programming, were first studied in the 1960s. MDPs can be ...
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Modeling Uncertainty
Modeling Uncertainty: An Examination of Stochastic Theory, Methods, and Applications, is a volume undertaken by the friends ...
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Risk-Averse Capacity Control in Revenue Management
“If necessity is the mother of invention, then deregulation is the father, and r- enue management (also known as yield ...
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Stochastic Learning and Optimization
(Four areas in one book) This book covers various disciplines in learning and optimization, including perturbation analysis ...
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