Bayesian Networks and Decision Graphs
Probabilistic graphical models and decision graphs are powerful modeling tools for reasoning and decision making under uncertainty. ...
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Bayesian Networks and Decision Graphs
Probabilistic graphical models and decision graphs are powerful modeling tools for reasoning and decision making under uncertainty. ...
Lire la suiteDistributed 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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