Digital Signal Processing Reference
In-Depth Information
Fig. A.8
Bernoulli
distribution for
p
=
0.6
p(x)
0.4
0.5
0.4
0.3
0.2
0.1
0
0
1 x
Æ
p
;
for
x
=
1
=
p
(
x
)
(A.11)
(1
−
p
)
for
x
=
0
A.5.2.2 Gaussian Distribution
1
√
2
2
e
−
(
x
−
μ
)
2
2
/
2
σ
p
(
x
)
=
(A.12)
πσ
μ
σ
where,
is the standard deviation.
2-D Gaussian can also be defined as
is the mean and
2
e
−
(
x
−
μ
)
2
2
1
2
)
2
/
2
σ
+
(
y
−
μ
/
2
σ
p
(
x
,
y
)
=
√
2
(A.13)
πσ
A.5.2.3. Poisson Distribution
x
e
−
λ
x
λ)
=
λ
p
(
x
,
;
λ
=
0, 1, 2
......
(A.14)
!
where
λ
is the occurrence interval [4].
A.5.2.4 Rayleigh Distribution
2
exp
x
2
2
x
σ
=
−
p
(
x
)
(A.15)
2
σ
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