# ECE 534: Elements of Information Theory, Fall 2011 Homework 1 ```ECE 534: Elements of Information Theory, Fall 2011
Homework 1
Solutions
Problem 2.2 (Hamid Dadkhahi) Let Y = g(X). Suppose PX (x) and PY (y) denote the probability mass functions of random variables X and Y , respectively. P
If g is an injective mapping,
then PY (y) = PX (x) and hence H(Y ) = H(X). Otherwise, PY (y) = x:g(x)=y PX (x). In this case,
since multiple intervals in X are merged to generate one interval in Y , the entropy of Y is smaller
than that of X.
(a) H(Y ) = H(X), because g(X) = 2X is an injective map.
(b) H(Y ) ≤ H(X), where we have equality if the domain of X is contained in [k π2 , (k + 1) π2 ] (k is
an integer). Otherwise, the map g(X) = cos(X) is not injective and H(Y ) is smaller than H(X).
Problem 2.6 (Hamid Dadkhahi) (a) If Z = X and Y is chosen such that H(X|Y ) &lt; H(X)
(i.e. X is not independent of Y ; for instance Y = X results in H(Y |X) = 0), then I(X; Y |Z) = 0,
and I(X; Y ) &gt; 0. Therefore, I(X; Y |Z) &lt; I(X; Y ).
(b) Suppose X and Y are independent of each other, and Z = g(X, Y ), where the function g is
chosen such that it cannot be solved for X without the knowledge of Y (and can be solved for X
with the knowledge of Y ). In this case, we have: I(x; Y ) = H(X) − H(X|Y ) = H(X) − H(X) = 0
and I(X; Y |Z) = H(X|Z) − H(X|Y, Z) = H(X|Z) − 0 = H(X|Z). H(X|Y, Z) = 0 because given
Y , Z = g(X, Y ) can be solved for X. H(X|Z) &gt; 0 since Z cannot be solved for X without the
knowledge of Y . Two examples of this are given in textbook (page 35) and Mackay’s book (the
page included in the course slides).
Problem 2.11 (Hamid Dadkhahi) Since X1 and X2 are identically distributed, we have H(X1 ) =
H(X2 ).
(a)
H(X2 |X1 )
H(X1 )
H(X1 ) − H(X2 |X1 )
H(X1 )
H(X2 ) − H(X2 |X1 )
H(X1 )
I(X1 ; X2 )
.
H(X1 )
ρ = 1−
=
=
=
(b) Since both mutual information and entropy are non-negative identities, we have ρ ≥ 0. Thus,
1
it suffices to show ρ ≤ 1:
I(X1 ; X2 ) = H(X2 ) − H(X2 |X1 )
≤ H(X2 )
= H(X1 ).
Dividing both sides of the inequality I(X1 ; X2 ) ≤ H(X1 ) by H(X1 ), yields ρ ≤ 1.
(c) ρ = 0, when I(X1 ; X2 ) = 0, which occurs when X1 and X2 are independent of each other.
(d) ρ = 1, when I(X1 ; X2 ) = H(X1 ) = H(X2 ), or equivalently H(X2 |X1 ) = H(X2 ), which occurs
when knowledge of one random variable results in the knowledge of the other. In other words, X1
and X2 are dependent.
Problem 2.12 (Pongsit Twichpongtorn)
Problem 2.12
p(x,y)
p(x=0)
p(x=1)
p(y=0)
p(y=1)
1
3
1
3
1
3
0
(a) H(X), H(Y )
H(X) = −
X
p(x) log p(x)
2
1
1
2
= −( ) log( ) − ( ) log( )
3
3
3
3
= 0.918 = H(Y )
Figure 1: H(X), H(Y )
2
(b) H(X|Y ), H(Y |X)
H(X|Y ) =
X
p(y)H(X|Y )
= p(Y = 0)H(X|Y = 0) + p(Y = 1)H(X|Y = 1)
1
2
1 1
= H(1) + H( , )
3
3
2 2
2
1
1
1
1
= 0 + (−( ) log( ) − ( ) log( ))
3
2
2
2
2
2
2 1 1
= ( + ) = = 0.667
3 2 2
3
Figure 2: H(X|Y )
H(Y |X) =
X
p(x)H(Y |X)
= p(X = 0)H(Y |X = 0) + p(X = 1)H(Y |X = 1)
1
1 1
2
= H( , ) + H(1)
3
2 2
3
2
1
1
1
1
= (−( ) log( ) − ( ) log( )) + 0
3
2
2
2
2
= 0.667
Figure 3: H(Y |X)
(c) H(X, Y )
H(Y, X) = H(X) + H(Y |X) = H(Y ) + H(X|Y )
= 0.918 + 0.667 = 1.585
3
Figure 4: H(Y, X)
Figure 5: H(Y ) − H(Y |X)
(d) H(Y ) − H(Y |X)
H(Y ) − H(Y |X) = 0.918 − 0.667 = 0.251
(e) I(X; Y )
I(X; Y ) = H(X) − H(X|Y )
= H(Y ) − H(Y |X)
= 0.251
Figure 6: I(X; Y )
Problem 2.18 (Nan Xu)
Since A and B are equally matched and the games are independent.
(1) 2 situations: holding 4 games and then have a winner(AAAA,BBBB): Each happens with prob1
ability ( 12 )4 = 16
4
(2) 8 = 2 43 situations: holding 5 games and then have a winner(????A,????B): Each happens with
1
probability ( 12 )5 = 32
(3) 20 = 2 54 situations: holding 6 games and then have a winner(?????A,?????B): Each happens
1
with probability ( 21 )6 = 64
(4) 40 = 2 65 situations: holding 7 games and then have a winner(??????A,??????B): Each happens
1
with probability ( 12 )7 = 128
The probability of holding 4 games(Y=4) is 2( 12 )4 =
1
8
The probability of holding 5 games(Y=5) is 8( 12 )5 =
1
4
The probability of holding 6 games(Y=6) is 20( 21 )6 =
5
16
The probability of holding 7 games(Y=7) is 40( 21 )7 =
5
16
H(X) = −
X
p(x) log2 p(x)
1
1
1
1
) log2 16 + 8( ) log2 32 + 20( ) log2 64 + 40(
) log2 128
16
32
64
128
= 5.8125
X
H(Y ) = −
p(y) log2 p(y)
= 2(
1
5
16
16
1
5
log2 8 + log2 4 +
log2 ( ) +
log2 ( )
8
4
16
5
16
5
= 1.924
=
since X there is no randomness in Y,or Y is a deterministic function of X, so H(Y |X) = 0
since H(X) + H(Y |X) = H(Y ) + H(X|Y ), so H(X|Y ) = H(X) + H(Y |X) − H(Y ) = 3.8885
5
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