Correspondence between variational methods and Hidden Markov Models
作者： J. Ziehn , M. Ruf , B. Rosenhahn , D. Willersinn , J. Beyerer
关键词:
摘要: This paper establishes a duality between the calculus of variations, an increasingly common method for trajectory planning, and Hidden Markov Models (HMMs), probabilistic graphical model with applications in artificial intelligence machine learning. allows findings from each field to be applied other, namely providing efficient robust global optimization tool learning algorithms variational problems, fast local solution methods large state-space HMMs.
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