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Model-Based Processing
book

Model-Based Processing

by James V. Candy
March 2019
Intermediate to advanced content levelIntermediate to advanced
544 pages
15h 44m
English
Wiley
Content preview from Model-Based Processing

Glossary

ADC
analog‐to‐digital conversion
AIC
Akaike information criterion
AR
autoregressive (model)
ARMA
autoregressive moving average (model)
ARMAX
autoregressive moving average exogenous input (model)
ARX
autoregressive exogenous input (model)
AUC
area‐under‐curve (ROC curve)
BSP
Bayesian signal processing
BW
bandwidth
CD
central difference
CDF
cumulative distribution
CM
conditional mean
CRLB
Cramer–Rao lower bound
C‐Sq
Chi‐squared (distribution or test)
CT
continuous‐time
CTD
concentration–temperature–density (measurement)
CVA
canonical variate analysis
EKF
extended Kalman filter
EM
expectation–maximization
FPE
final prediction error
GLRT
generalized likelihood ratio test
G‐M
Gaussian mixture
GM
Gauss–Markov
G‐S
Gaussian sum
HD
Hellinger distance
HPR
high probability region
IEKF
iterated–extended Kalman filter
i.i.d.
independent‐identically distributed (samples)
KD
Kullback divergence
KL
Kullback–Leibler
KLD
Kullback–Leibler divergence
KSP
Kalman–Szego–Popov (equations)
LD
lower diagonal (matrix) decomposition
LE
Lyapunov equation
LKF
linear Kalman filter
LMS
least mean square
LS
least‐squares
LTI
linear, time‐invariant (system)
LZKF
linearized Kalman filter
MA
moving average (model)
MAICE
minimum Akaike information criterion
MAP
maximum a posteriori
MATLABimages
mathematical software package
MBID
model‐based identification
MBP
model‐based processor
MBSP
model‐based signal processing ...
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Publisher Resources

ISBN: 9781119457763Purchase book