 # Joint distributions

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Joint distributions
```PROBABILITY
where MXi (t) is the MGF of fi (x). Now
t
t2
t
MXi
= 1 + E[Xi ] + 12 2 E[Xi2 ] + · · ·
n
n
n
= 1 + µi
and as n becomes large
MXi
t
t2
+ 12 (σi2 + µ2i ) 2 + · · · ,
n
n
t
µi t 1 2 t2
+ 2 σi 2 ,
≈ exp
n
n
n
as may be veriﬁed by expanding the exponential up to terms including (t/n)2 .
Therefore
2 n
µi t 1 2 t2
σ
i µi
+ 2 σi 2 = exp
t + 12 i 2 i t2 .
exp
MZ (t) ≈
n
n
n
n
i=1
Comparing this with the form of the MGF for a Gaussian distribution, (30.114),
we can see that the probability density function g(z) of Z tends to a Gaussian dis
tribution with mean i µi /n and variance i σi2 /n2 . In particular, if we consider
Z to be the mean of n independent measurements of the same random variable X
(so that Xi = X for i = 1, 2, . . . , n) then, as n → ∞, Z has a Gaussian distribution
with mean µ and variance σ 2 /n.
We may use the central limit theorem to derive an analogous result to (iii)
above for the product W = X1 X2 · · · Xn of the n independent random variables
Xi . Provided the Xi only take values between zero and inﬁnity, we may write
ln W = ln X1 + ln X2 + · · · + ln Xn ,
which is simply the sum of n new random variables ln Xi . Thus, provided these
new variables each possess a formal mean and variance, the PDF of ln W will
tend to a Gaussian in the limit n → ∞, and so the product W will be described
by a log-normal distribution (see subsection 30.9.2).
30.11 Joint distributions
As mentioned brieﬂy in subsection 30.4.3, it is common in the physical sciences to
consider simultaneously two or more random variables that are not independent,
in general, and are thus described by joint probability density functions. We will
return to the subject of the interdependence of random variables after ﬁrst
presenting some of the general ways of characterising joint distributions. We
will concentrate mainly on bivariate distributions, i.e. distributions of only two
random variables, though the results may be extended readily to multivariate
distributions. The subject of multivariate distributions is large and a detailed
study is beyond the scope of this book; the interested reader should therefore
1196
30.11 JOINT DISTRIBUTIONS
consult one of the many specialised texts. However, we do discuss the multinomial
and multivariate Gaussian distributions, in section 30.15.
The ﬁrst thing to note when dealing with bivariate distributions is that the
distinction between discrete and continuous distributions may not be as clear as
for the single variable case; the random variables can both be discrete, or both
continuous, or one discrete and the other continuous. In general, for the random
variables X and Y , the joint distribution will take an inﬁnite number of values
unless both X and Y have only a ﬁnite number of values. In this chapter we
will consider only the cases where X and Y are either both discrete or both
continuous random variables.
30.11.1 Discrete bivariate distributions
In direct analogy with the one-variable (univariate) case, if X is a discrete random
variable that takes the values {xi } and Y one that takes the values {yj } then the
probability function of the joint distribution is deﬁned as
#
Pr(X = xi , Y = yj ) for x = xi , y = yj ,
f(x, y) =
0
otherwise.
We may therefore think of f(x, y) as a set of spikes at valid points in the xy-plane,
whose height at (xi , yi ) represents the probability of obtaining X = xi and Y = yj .
The normalisation of f(x, y) implies
f(xi , yj ) = 1,
(30.125)
i
j
where the sums over i and j take all valid pairs of values. We can also deﬁne the
cumulative probability function
F(x, y) =
f(xi , yj ),
(30.126)
xi ≤x yj ≤y
from which it follows that the probability that X lies in the range [a1 , a2 ] and Y
lies in the range [b1 , b2 ] is given by
Pr(a1 < X ≤ a2 , b1 < Y ≤ b2 ) = F(a2 , b2 ) − F(a1 , b2 ) − F(a2 , b1 ) + F(a1 , b1 ).
Finally, we deﬁne X and Y to be independent if we can write their joint distribution
in the form
f(x, y) = fX (x)fY (y),
i.e. as the product of two univariate distributions.
1197
(30.127)
PROBABILITY
30.11.2 Continuous bivariate distributions
In the case where both X and Y are continuous random variables, the PDF of
the joint distribution is deﬁned by
f(x, y) dx dy = Pr(x < X ≤ x + dx, y < Y ≤ y + dy),
(30.128)
so f(x, y) dx dy is the probability that x lies in the range [x, x + dx] and y lies in
the range [y, y + dy]. It is clear that the two-dimensional function f(x, y) must be
everywhere non-negative and that normalisation requires
∞ ∞
f(x, y) dx dy = 1.
−∞
−∞
It follows further that
b2
Pr(a1 < X ≤ a2 , b1 < Y ≤ b2 ) =
a2
f(x, y) dx dy.
b1
a1
(30.129)
We can also deﬁne the cumulative probability function by
x y
f(u, v) du dv,
F(x, y) = Pr(X ≤ x, Y ≤ y) =
−∞
−∞
from which we see that (as for the discrete case),
Pr(a1 < X ≤ a2 , b1 < Y ≤ b2 ) = F(a2 , b2 ) − F(a1 , b2 ) − F(a2 , b1 ) + F(a1 , b1 ).
Finally we note that the deﬁnition of independence (30.127) for discrete bivariate
distributions also applies to continuous bivariate distributions.
A ﬂat table is ruled with parallel straight lines a distance D apart, and a thin needle of
length l < D is tossed onto the table at random. What is the probability that the needle
will cross a line?
Let θ be the angle that the needle makes with the lines, and let x be the distance from
the centre of the needle to the nearest line. Since the needle is tossed ‘at random’ onto
the table, the angle θ is uniformly distributed in the interval [0, π], and the distance x
is uniformly distributed in the interval [0, D/2]. Assuming that θ and x are independent,
their joint distribution is just the product of their individual distributions, and is given by
f(θ, x) =
1 1
2
=
.
π D/2
πD
The needle will cross a line if the distance x of its centre from that line is less than 12 l sin θ.
Thus the required probability is
2
πD
π
0
1
l sin θ
2
0
dx dθ =
2 l
πD 2
π
sin θ dθ =
0
2l
.
πD
This gives an experimental (but cumbersome) method of determining π. 1198
```
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