12.864 Inference from Data and Models, Spring 2003
Name
12-864Spring2003/OcwWeb/Earth--Atmospheric--and-Planetary-Sciences/12-864Spring2003/CourseHome/index.htm
Size
14.44 KB
Format
HTML
Checksum (MD5)
9081d4cc4637e4fa9597eac17d582c24
Author(s)
Wunsch, Carl
Alternative Title
Inference from Data and Models
Date Issued
June 2003
Abstract
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.
Subjects
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
MIT Department
Massachusetts Institute of Technology. Department of Earth, Atmospheric, and Planetary Sciences
Terms of Use
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