6.438 -- Algorithms for Estimation and Inference
Course Description: Estimation and inference problems arising in signal processing, optimization and control, and machine learning. Second-order characterizations of random phenomena. Least squares estimation: Orthogonality, and whitening; Wiener filtering; estimation for state space models: Kalman filters and smoothers, properties, and efficient algorithms. Model estimation: ergodicity, spectral estimation, likelihood calculation, all-pole models and the Levinson algorithm. Estimation for Markov models: particle filters, Viterbi algorithm. Markov random fields and graphical models: Belief Propagation Algorithms and properties; exponential families, variational methods, and max-entropy modeling.

This class is at the Graduate level
Instructor: P. Golland, A. S. Willsky, G. W. Wornell
Prerequisites: 6.011 and 18.06

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