Abstract / truncated to 115 words (read the full abstract)

Probabilistic graphical models are a powerful concept for representing complex relations between components in systems with uncertain behavior. Stemming originally from statistical mechanics, their applications stretch across various fields such as computer vision, speech recognition, social network analysis, and many more. Often they allow for a compact formulation of practical challenges in terms of fundamental computational problems, that are summarized under the term probabilistic inference. Unfortunately, these problems (e.g., the computation of marginal probabilities and the partition function) are computationally intractable so that we need to approximate the solution. In this thesis we consider two different categories of deterministic approximation methods: message passing algorithms and (variational) free energy approximations. Specifically, we focus on the most ... toggle 4 keywords

probabilistic graphical models – approximate inference – statistical mechanics – signal denoising

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