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The central objective of this thesis is to develop new algorithms for inference in probabilistic graphical models that improve upon the state-of-the-art and lend new insight into the computational nature of probabilistic inference. The four main technical contributions of this thesis are: 1) a new framework for inference in probabilistic models based on stochastic approximation, variational methods and sequential Monte Carlo is proposed that achieves significant improvements in accuracy anddoi:10.14288/1.0051537 fatcat:6dxbpekyznas3afs2iqhpdh6ae