Bu makalede gürültülü verilerden sinyallerin parametrelerinin kestirimini dü
ş
ünüyoruz. Bu
amaç için, Monte-Carlo simülasyonlar
ı
ile birlikte en çok olabilirlik prensibinin kullan
ı
m
ı
na
dayanan etkili bir yöntemin uyguland
ı
ğ
ı
bir
Mathematica
program
ı
yaz
ı
ld
ı
ve Gauss da
ğ
ı
l
ı
ml
ı
gürültülerle bozulmu
ş
sinüzoitlerin parametrelerinin kestirimi için kullan
ı
ld
ı
.
We consider here estimating parameters of signals from noisy data. For this purpose, a Mathematica program, in which an effective method based on the principle of maximum likelihood together with Monte-Carlo simulations is incorporated, was written and used for estimating the parameters of sinusoids corrupted by the Gaussian random noiseWe consider here estimating parameters of signals from noisy data. For this purpose, a
Mathematica
program, in which an effective method based on the principle of maximum
likelihood together with Monte-Carlo simulations
is incorporated, was written and used for
estimating the parameters of sinusoids co
rrupted by the Gaussian random noiseWe consider here estimating parameters of signals from noisy data. For this purpose, a
Mathematica
program, in which an effective method based on the principle of maximum
likelihood together with Monte-Carlo simulations
is incorporated, was written and used for
estimating the parameters of sinusoids co
rrupted by the Gaussian random noiseWe consider here estimating parameters of signals from noisy data. For this purpose, a
Mathematica
program, in which an effective method based on the principle of maximum
likelihood together with Monte-Carlo simulations
is incorporated, was written and used for
estimating the parameters of sinusoids co
rrupted by the Gaussian random noise