12.864 Inference from Data and Models, Spring 2003
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Alternative title
Inference from Data and Models
Metadata
Show full item recordAbstract
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.
Date issued
2003-06Department
Massachusetts Institute of Technology. Department of Earth, Atmospheric, and Planetary SciencesOther identifiers
12.864-Spring2003
local: 12.864
local: IMSCP-MD5-48c09642fe158d3d2e6e68a651d75886
Keywords
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