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