12.864 Inference from Data and Models, Spring 2003
Inference from Data and Models
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Fundamental methods used for exploring the information content of observations related to kinematical and dynamical models. Basic statistics and linear algebra for inverse methods including singular value decompositions, control theory, sequential estimation (Kalman filters and smoothing algorithms), adjoint/Pontryagin principle methods, model testing, etc. Second part focuses on stationary processes, including Fourier methods, z-transforms, sampling theorems, spectra including multi-taper methods, coherences, filtering, etc. Directed at the quantitative combinations of models, with realistic, i.e. sparse and noisy observations.
observation, kinematical models, dynamical models, basic statistics, linear algebra, inverse methods, singular value decompositions, control theory, sequential estimation, Kalman filters, smoothing algorithms, adjoint/Pontryagin principle methods, model testing, stationary processes, Fourier methods, z-transforms, sampling theorems, spectra, multi-taper methods, coherences, filtering, quantitative combinations, realistic observations, data assimilations, deduction, regression, objective mapping, time series analysis, inference