Probability and stochastic processes 3rd solution, Lecture notes of Probability and Stochastic Processes

Chapter 3 exercise problems eeeeeeeeeeee

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2018/2019

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}.Discrete Random Variables (0kt ZTE HIF)
*Sx :the range of X,the set of all possible values of X
*discrete RV .
:Sx is acountable set .god 't
*Probability Mass function (PMF ):Px Ix)=PfXm=x
]
HEIKE
Ex .3. 5) Two free shots ,X:the #of points
Sx =40 ,I,2}X0I2
gpun -
-IPNM II&
asample
spat's "
orange , !
'÷÷
,elw I-.---.-..
>
OI2K
(p)
Bernoulli R.V. :114 Hbk 'VE an WEE 'EE ,Sx -
-to ,It
17×64=1 tpp ,
K'of .sub experiments with two possible outcomes
,at :Bernoulli trials
O,elw
Ip)
It Geometric R.V.
:21¥ event HINT Mnt HHbk ELF ,Sx -
-{I,2,3 .
. -.}
P,Ix,=fP(I-P)""
,
a- I
,2,3 ...
0
,elw
(n,p)
Binomial RV .
:ksuccesses in ntrials
Px (k)=f(I)Pk Ctp )
""
,
k=o ,1,2 ,
...
,
n
O,elw
In,K)
*Pascal R.V.
:Htt event kik KHANH Alby Zdf
x
Px Ia)=fIKI )pkltpl "'
,
x=k ,KH ,
...
O
,elw
pf3
pf4
pf5
pf8

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} . Discrete Random^ Variables^ ( 0kt^ ZTE^ HIF)

* Sx :^ the^

range

of X

the set (^) of all^ possible values^ of X

* discrete RV .^ :^ Sx is a countable set .

god

't
  • (^) Probability Mass^ function^ (^ PMF^ )^ :^ Px^ I^ x^ )^ = PfXm=x] HEIKE Ex (^).^ 3.^ 5) Two free shots (^) , X :^ the #^ of (^) points Sx =^40 , I (^) , 2 } (^) ② X 0 I 2 g ① (^) pun -^ -^ I^ PNM I I & a (^) samplespat's "

orange ,^

4¥! '÷÷

, elw^ I

-. (^) - - -. - (^)..

O I 2 K

( p )

Bernoulli R (^).^ V^.^ :^114 Hbk^ 'VE (^) an WEE^ ' EE

Sx -^ - to (^) , It 17×64=1 tpp^ , K ' of. sub

experiments

with two^ possible outcomes

,^ at^

: Bernoulli trials

O (^) , elw I (^) p ) It Geometric R^. V^.^ :^ 21¥^ event^ HINT^ Mnt^ H^ Hbk^ ELF^ ,^ Sx^

    • { I , 2,^. . -. }

P

, I^ x^

, = f P ( I^ - P ) " "

a- I (^) , 2,.^.^. (^0) , elw ( n^ , p ) Binomial RV^.^ :^ k^ successes^ in n^ trials

Px ( k) =

f ( (^) I ) Pk^ Ctp^ )^ " (^) "

k=o (^) , 1,2 (^) ,^.^.^. ,^ n O (^) , elw

I n^ , K^ )

  • (^) Pascal R. V^.^ :^ Htt^ event^ kik^ KHANH^ Alby^ Zdf
x

Px I a^ ) =

f I KI^ ) (^) pkltpl

, x=k (^) , KH (^) ,^.^.^. O (^) , elw

( k^ ,^ l^ )

  • discrete^ uniform RV^.^ :^ equiprobable outcomes

PNC n^ ) =

{ HCl -^ KH^ )^ , n=k (^) ,^ KH^ ,^.^.^. ,^ l

O

, elw

  • PoissonR. V^.^ :^ Natatorial^ event^ KITE^ the p, = ya " e
  • Ha!^ i^ "^ ⇒^ ' '^ '^2 '^ '^ "

d =

II time interval

O

, elw

average rate

arrivalshed μ 3.4 (^).^ Cumulative^ Distribution^ Function^ (^ CDF^ ) Film =^ PCXE^ x^ )

Ex 3.^ lb ) CDF of

geometric (1/4) R.V.^?

Py Ly ,.

. { (1/4) (3/4) 't '

, y

      1. (^) 2,3 , - (^) - - O (^) ,^ elw Film

II Pay ,^ = FI ,

GET^

= if =L - ¥ " integer : (^). Fycnyy = f

0 , y s^ I

real (^) I - ¥ , y z , floor function

Averages and^ Expected^ Value

* EH

)=μ×=¥s×x

  • Pxl "

ex ) Bernoulli^ :^ ELM^

    • O. Ctplt I -

p =p

ex (^) ) geometric : ECXI^

    • Faik

Pll - pl

" (^) = Yp ←

power

series

ex ) Poisson^ :^ EIA^

= II. xd^

e

%!^ =^ a^ ←^ total^ sum^ of^ PMF

ex ) binomial^ :^ EH^ )^

    • Eon P' ' Aptn - " = rip I

ex ) Pascal : ECM =

Eif pka^

  • p ) "^ "^ =

kyp

  • MSE (^) ( Mean (^) Square Error^ ) → (^) e = (^) ELK E)- 2) ① the^ minimum MSE^ estimate^ = (^) I = (^) ELX ) ② the^ minimum MSE^ =^ EKX-^ EA)^ )^ ' ] =^ Var^ CX)
  • (^) Varix ] = ELM
  • END f .
  • (^) nth moment =^ EH^ " ] →^ 1st^ moment^ =^ THE nth central^ moment^ =^ El^ # Y^ ]^ →^ 2nd^ central^

moment =^ Hit

R (^) R R (^) R R (^) R O (^) O (^) O O O (^) o X (^) x x x (^) ×

IB

Cbl (^) Px IN -^ -

g

Pll

pl

, a- I (^) ,^ -^ - ; 5 ( I - p ) 5 I

o , ecw

cc)^ (^ I^ - pH N (d) ( I^ -^ 0.9^ )^ L^ o^.^02 10 "

i (^). n Z 2

Ca)^ PL, (d) =^ (F) (^) ,^ f-^ 91.^. ' (^) " { (^) o ,^ elw

(b) PCW -10 ) =

(f)

to

Pascal (^) R (^). V (^). ( kilo^ )

i. pwcws = (

wt )

fEY

"

w.io , " ,^.^.^.

10 - I l { o , eh SIC (a) Puny

    • f (^) !! ) IIT^

      ,

k= 3. 4.

{ o (^) , elw gtw ) Kd^ Rtm IHH 't (b) Pwlwi

-11791- 't'T^ , a- (^) on .z

w =3 (^) ( EY (^) ) O (^) , elw ' " kai-yletek.gs#e.ii:i!au 0.5 (^) ,^ l^ =3^ ( EU ) O (^) ,^ elw

Pxnlkl =/na^ ) p 'll^

  • pH? x -^ - on (^) , (^2) ,^.^ - in { o (^) , elw Eun ]

II.

x P'

a - pin

=

saints x. . P

a -

p "^

n (^) n. ( n - I (^) )!

= I -

⇐ I @YHkn-p.ca^

P

' P'

Ctp) " - Htt = npE.it:^ P' " HPT

, a np.E.it:7/p4i-psn- " " = np

. II. (^) Pain = np ELM = IE KPCA-kf-PCADtz.pl/=Dtz.PCx=3It--=pCX=D = PIX >^ oft PIX >^ Dt PIX^ >^21 t^

  • " t (^) plant PH 's^ ) (^) = II.oplxsk) tplx-3HPH-htpcx.SI

(a) ¥= Emo (^) : (^).

p

    • Tomo PR (^) ( r^ ) = g Tomo (^) ( I^ - Tomo) " (^) "
,^ 2=1^ ,^ 2,^
  • -^. O (^) , elw (b) (^) EIR ]= I ome Efw ]^

= Ef5mRT= 5 MEIR) = 500

A- I^2 3 (a) (^) ° × Ox^ Ox^ Ox^

. (^) ' Px (^) I x (^) ) = f ( (^) tf ) (^) of " ,^ A-^ I.^ 2.^.

  • -. O (^) , elw ( (^) b ) (^) X -^ - I →^ 7=1 m

X=2 →^ F- 3m

} T = 3¥ 11=3 →^ T =^ 5ns

1000T - I
÷

Pt =

( I - G)

q= , t =^ Im^ ,^ 3m^ , 5M (^) ,^.^.^. O (^) , elw ord : Pr ( H=^ 0. "

r -^ - k - to

{ I^ -^ 0.^ "

r= -^ to

° (^) , elw ultra :^ PRIM^ = g

,^ r=^ K^

  • 30 I - 0.9510 r =^ - I

0 , elw

ELR)^ ord - ECR)^ ultra = (^) ( k-1070.

t (-1014-0.910) - f Ck^ -3070.95 't^ (-3074-0.950)}

Tff

mm run^ mm 0.65 0.60^ 0.

0.25k^ I^ - t

. :^ KC^80 ⇒^ ordinary , k^ >^

80 ⇒ ultra -^ reliable

k

Pk (^ k^ )^ =^ X^ e-^ 4k^!^ ,^ k=0^ , 1,2^ ,

  • (^)..

{ o

, elw Elk (^) ] = EEK ' -4k^ ake ! = DIE

the -4*1 (^) ,! =^ x

K -1=

Varlk) =^ Elle^ )^ -^ (ECK))

! Elk C Kt ) ] TECK ) - LECK] )

' = (^) data - as =^ a

ELKCK - H) = II Hk^ -^ Dake^ -4k^!^ =L^ the^ -4*-2 , I^ =D^

K -2=