30.9 IMPORTANT CONTINUOUS DISTRIBUTIONS
expression for the PDF of thisZ does exist, but it is a rather complicated
combination of exponentials and a modified Bessel function.§
Two types of e-mail arrive independently and at random: external e-mails at a mean rate
of one every five minutes and internal e-mails at a rate of two every five minutes. Calculate
the probability of receiving two or more e-mails in any two-minute interval.
Let
X= number of external e-mails per two-minute interval,
Y= number of internal e-mails per two-minute interval.
Since we expect on average one external e-mail and two internal e-mails every five minutes
we haveX∼Po(0.4) andY∼Po(0.8). LettingZ=X+Ywe haveZ∼Po(0.4+0.8) =
Po(1.2). Now
Pr(Z≥2) = 1−Pr(Z<2) = 1−Pr(Z=0)−Pr(Z=1)
and
Pr(Z=0)=e−^1.^2 =0. 301 ,
Pr(Z=1)=e−^1.^2
1. 2
1
=0. 361.
Hence Pr(Z≥2) = 1− 0. 301 − 0 .361 = 0.338.
The above result can be extended, of course, to any number of Poisson processes,
so that ifXi=Po(λi),i=1, 2 ,...,nthen the random variableZ=X 1 +X 2 +
···+Xnis distributed asZ∼Po(λ 1 +λ 2 +···+λn).
30.9 Important continuous distributions
Having discussed the most commonly encountered discrete probability distri-
butions, we now consider some of the more important continuous probability
distributions. These are summarised for convenience in table 30.2; we refer the
reader to the relevant subsection below for an explanation of the symbols used.
30.9.1 The Gaussian distribution
By far the most important continuous probability distribution is theGaussian
ornormaldistribution. The reason for its importance is that a great many
random variables of interest, in all areas of the physical sciences and beyond, are
described either exactly or approximately by a Gaussian distribution. Moreover,
the Gaussian distribution can be used to approximate other, more complicated,
probability distributions.
§For a derivation see, for example, M. P. Hobson and A. N. Lasenby,Monthly Notices of the Royal
Astronomical Society, 298 , 905 (1998).