Variational Methods for Graph Models with Hidden Variables




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In this thesis, we focus on the Markov Random Field graph. We gradually transition to Hidden Markov Model and work exclusively with binary graphs. We then utilize produced graphical model to train machine learning model that would classify images of written digits. The thesis is comprised of two parts: review of the articles and implementation (coding) of the key models and methods introduced in the articles. The articles are chosen from the seminal work as well as from the recent advances in graphical models, graphical models with latent variables and their application to various image recognition problems. The last part of the thesis applies developed inference framework to produce machine learning model for written digits recognition task.



Markov random fields, Graph theory, Hidden Markov models, Machine learning, Graphic methods


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