12 1. Preparatory material
Now we turn to non-discrete random variables X taking values in some
range R. We say that a random variable is continuous if P(X = x) = 0 for
all x R (here we assume that all points are measurable). If R is already
equipped with some reference measure dm (e.g., Lebesgue measure in the
case of scalar, vector, or matrix-valued random variables), we say that the
random variable is absolutely continuous if P(X S) = 0 for all null sets
S in R. By the Radon-Nikodym theorem (see e.g., [Ta2010, §1.10]), we can
thus find a non-negative, absolutely integrable function f
L1(R,
dm) with
R
f dm = 1 such that
(1.5) μX(S) =
S
f dm
for all measurable sets S R. More succinctly, one has
(1.6) dμX = f dm.
We call f the probability density function of the probability distribution μX
(and thus, of the random variable X). As usual in measure theory, this
function is only defined up to almost everywhere equivalence, but this will
not cause any difficulties.
In the case of real-valued random variables X, the distribution μX can
also be described in terms of the cumulative distribution function
(1.7) FX(x) := P(X x) = μX((−∞,x]).
Indeed, μX is the Lebesgue-Stieltjes measure of FX , and (in the absolutely
continuous case) the derivative of FX exists and is equal to the probability
density function almost everywhere. We will not use the cumulative distri-
bution function much in this text, although we will be very interested in
bounding tail events such as P(X λ) or P(X λ).
We give some basic examples of absolutely continuous scalar distribu-
tions:
(i) uniform distributions, in which f :=
1
m(I)
1I for some subset I of
the reals or complexes of finite non-zero measure, e.g., an interval
[a, b] in the real line, or a disk in the complex plane.
(ii) The real normal distribution N(μ,
σ2)
= N(μ,
σ2)R
of mean μ
R and variance
σ2
0, given by the density function f(x) :=
1

2πσ2
exp(−(x
μ)2/2σ2)
for x R. We isolate, in particular,
the standard (real) normal distribution N(0, 1). Random variables
with normal distributions are known as Gaussian random variables.
(iii) The complex normal distribution N(μ,
σ2)C
of mean μ C and
variance
σ2
0, given by the density function f(z):=
1
πσ2
exp(−|z
μ|2/σ2).
Again, we isolate the standard complex normal distribu-
tion N(0, 1)C.
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