Probabilistic graphical models and decision graphs are powerful modeling tools for reasoning and decision making under uncertainty. ...
Lire la suiteProbabilistic graphical models and decision graphs are powerful modeling tools for reasoning and decision making under uncertainty. ...
Lire la suiteThis book constitutes the refereed proceedings of the Second International Conference on Distributed Artificial Intelligence, ...
Lire la suiteThis book constitutes the thoroughly refereed and peer-reviewed outcome of the Formal Methods and Testing (FORTEST) network ...
Lire la suiteThe European Conference on Machine Learning (ECML) and the European Conference on Principles and Practice of Knowledge Discovery ...
Lire la suiteMarkov chains are a particularly powerful and widely used tool for analyzing a variety of stochastic (probabilistic) systems ...
Lire la suiteMarkov chains are a particularly powerful and widely used tool for analyzing a variety of stochastic (probabilistic) systems ...
Lire la suiteMarkov decision processes (MDPs), also called stochastic dynamic programming, were first studied in the 1960s. MDPs can be ...
Lire la suiteModeling Uncertainty: An Examination of Stochastic Theory, Methods, and Applications, is a volume undertaken by the friends ...
Lire la suite“If necessity is the mother of invention, then deregulation is the father, and r- enue management (also known as yield ...
Lire la suite(Four areas in one book) This book covers various disciplines in learning and optimization, including perturbation analysis ...
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