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应用随机过程概率模型导论 9版答案
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应用随机过程概率模型导论 Sheldon Ross 9版答案
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Instructor’s Manual to Accompany
Introduction to
Probability Models
Ninth Edition
Sheldon M. Ross
University of California
Berkeley, California
AMSTERDAM
•
BOSTON
•
HEIDELBERG
•
LONDON
NEW YORK
•
OXFORD
•
PARIS
•
SAN DIEGO
SAN FRANCISCO
•
SINGAPORE
•
SYDNEY
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TOKYO
Academic Press is an imprint of Elsevier
Academic Press is an imprint of Elsevier
30 Corporate Drive, Suite 400, Burlington, MA 01803, USA
525 B Street, Suite 1900, San Diego, California 92101-4495, USA
84 Theobald’s Road, London WC1X 8RR, UK
Copyright
c
2007, Elsevier Inc. All rights reserved.
No part of this publication may be reproduced or transmitted in any form or by any means, electronic or
mechanical, including photocopy, recording, or any information storage and retrieval system, without
permission in writing from the publisher.
Permissions may be sought directly from Elsevier’s Science & Technology Rights Department in Oxford,
UK: phone: (+44) 1865 843830, fax: (+44) 1865 853333, E-mail: permissions@elsevier.com. You may also
complete your request on-line via the Elsevier homepage (http://elsevier.com), by selecting “Support & Contact”
then “Copyright and Permission” and then “Obtaining Permissions.”
ISBN 13: 978-0-12-373875-2
ISBN 10: 0-12-373875-X
For information on all Academic Press publications
visit our Web site at www.books.elsevier.com
Printed in the United States of America
0607080910987654321
Contents
Chapter 1 ....................................................................1
Chapter 2 ....................................................................7
Chapter 3 ...................................................................17
Chapter 4 ...................................................................33
Chapter 5 ...................................................................43
Chapter 6 ...................................................................59
Chapter 7 ...................................................................71
Chapter 8 ...................................................................81
Chapter 9 ...................................................................95
Chapter 10 ..................................................................99
Chapter 11 .................................................................105
Chapter 1
1. S = {(R, R), (R, G) , (R, B), (G, R), (G, G), (G, B),
(B, R), (B, G), (B, B)}.
The probability of each point in S is 1/9.
2. S
= {(R, G), (R, B), (G, R), (G, B), (B, R), (B, G)}.
3. S
= {(e
1
, e
2
,...,e
n
), n ≥ 2} where e
i
∈ (heads,
tails}. In addition, e
n
= e
n−1
= heads and for
i
= 1,...,n − 2ife
i
= heads, then e
i+1
= tails.
P
{4 tosses} = P{(t, t, h, h)} + P{(h, t, h, h)}
=
2
1
2
4
=
1
8
.
4. (a) F
(E ∪ G)
c
= FE
c
G
c
.
(b) EFG
c
.
(c) E
∪ F ∪ G.
(d) EF
∪ EG ∪ FG.
(e) EFG.
(f)
(E ∪ F ∪G)
c
= E
c
F
c
G
c
.
(g)
(EF)
c
(EG)
c
(FG)
c
.
(h)
(EFG)
c
.
5.
3
4
. If he wins, he only wins $1, while if he loses, he
loses $3.
6. If E
(F ∪G) occurs, then E occurs and either F or G
occur; therefore, either EF or EG occurs and so
E
(F ∪G) ⊂ EF ∪ EG.
Similarly, if EF
∪ EG occurs, then either EF or EG
occur. Thus, E occurs and either F or G occurs; and
so E(F
∪ G) occurs. Hence,
EF
∪ EG ⊂ E(F ∪ G),
which together with the reverse inequality proves
the result.
7. If
(E ∪ F)
c
occurs, then E ∪ F does not occur, and
so E does not occur (and so E
c
does); F does not
occur (and so F
c
does) and thus E
c
and F
c
both
occur. Hence,
(E ∪ F)
c
⊂ E
c
F
c
.
If E
c
F
c
occurs, then E
c
occurs (and so E does not),
and F
c
occurs (and so F does not). Hence, neither
E or F occur and thus
(E ∪ F)
c
does. Thus,
E
c
F
c
⊂ (E ∪F)
c
and the result follows.
8. 1
≥ P(E ∪ F)=P(E)+P(F) − P( EF).
9. F
= E ∪ FE
c
, implying since E and FE
c
are disjoint
that P
(F)=P(E)+P(FE)
c
.
10. Either by induction or use
n
∪
1
E
i
= E
1
∪ E
c
1
E
2
∪ E
c
1
E
c
2
E
3
∪···∪E
c
1
···E
c
n
−1
E
n,
and as each of the terms on the right side are
mutually exclusive:
P
(∪
i
E
i
)=P(E
1
)+P(E
c
1
E
2
)+P(E
c
1
E
c
2
E
3
)+···
+
P(E
c
1
···E
c
n
−1
E
n
)
≤
P(E
1
)+P(E
2
)+···+ P(E
n
). (why?)
11. P{sum is i} =
i
−1
36
, i
= 2,...,7
13
−i
36
, i
= 8,...,12.
12. Either use hint or condition on initial outcome as:
P
{E before F}
=
P{E before F | initial outcome is E}P(E)
+
P{E before F | initial outcome is F}P(F)
+
P{E before F | initial outcome neither E
or F}[1 − P(E) − P(F)]
1
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