Stochastic models and their distributions

Transcription

1 Sochasic models and heir disribuions Couning cusomers Suppose ha n cusomers arrive a a grocery a imes, say T 1,, T n, each of which akes any real number in he inerval (, ) equally likely The values T 1,, T n are called arrival imes, and hey are independen and idenically disribued (iid) uniform random variables on (, ) By N(s) we denoe he number of cusomers arriving in he ime inerval (, s) for s Probabiliy densiy funcion The probabiliy densiy funcion (pdf) for each arrival ime is given by { 1/ if < x < ; f(x) oherwise [ha is, if x or x] The cusomers independenly arrive in he ime inerval (, s) wih probabiliy p : ( s ) Then he frequency funcion for N(s) is given by P {N(s) k} ( n k ) (s ) k ( 1 s ) n k, (11) and i is called a binomial disribuion wih success probabiliy p ( s ) and he number n of rials Order saisics and arrival ime Le X 1,, X n be iid random variables wih he common cumulaive disribuion funcion (cdf) F (x) and he pdf f(x) When we sor X 1,, X n as X (1) < X (2) < < X (n), he random variable X (k) is called he k-h order saisic Then we have he following heorem Theorem The pdf of he k-h order saisic X (k) is given by f k (x) (n k)! f(x)f k 1 (x)[1 F (x)] n k (12) Probabiliy of small inerval he proof of heorem If f is coninuous a x and δ > is small, hen he probabiliy ha a random variable X falls ino he small inerval [x, x + δ] is approximaed by δf(x) In differenial noaion, we can wrie P (x X x + dx) f(x)dx Now consider he k-h order saisic X (k) and he even ha x X (k) x + dx This even occurs when (k 1) of X i s are less han x and (n k) of X i s are greaer han x x + dx Since here are ( n k 1,1,n k) arrangemens for Xi s, we can obain he probabiliy of his even as which implies (12) f k (x)dx (n k)! (F (x))k 1 (f(x)dx)[1 F (x)] n k, Page 1 Special lecure/june 216

2 Bea disribuion In paricular when X 1,, X n be iid uniform random variables on [, 1], he pdf of he k-h order saisic X (k) becomes f k (x) (n k)! xk 1 (1 x) n k, x 1, which is called bea disribuion wih parameers α k and β n k + 1 Now consider he arrival imes T 1,, T n in our model and heir ordered saisics T (1) < < T (n) Then i is no hard o see ha T (k) has a bea disribuion wih (k, n + 1 k) Poisson approximaion The Poisson disribuion wih parameer λ (λ > ) has he frequency funcion λ λk p(k) e, k, 1, 2, and may be used as an approximaion for a binomial disribuion wih parameer (n, p) if n is large and p is small enough so ha np is a moderae number λ Le λ np Then he binomial frequency funcion becomes p(k) (n k)! λk 1 e λ λ λk 1 e ( ) k ( λ 1 λ ) n k λk n n as n ( (n k)!n 1 λ n ( 1 n) λ ) k k n In he previous model λ ( n) represens he average number of cusomers per hour If p s is small enough, we can apply he Poisson approximaion o ge λs n λs (λs)k P {N(s) k} e Problem 1 Suppose ha X and Y are independen random variables disribued as Poisson disribuions wih he respecive parameers α and β Le N X +Y Calculae he condiional probabiliy P (X k N n) Problem 2 Suppose ha a random variable N has a Poisson disribuion wih parameer λ, and ha a random variable X is condiionally disribued as a binomial disribuion wih parameer (n, p) given N n Le Y N X (a) Calculae P (X j, Y k) (b) Show ha boh X and Y have Poisson disribuions (c) Show ha X and Y are independen Poisson process Maybe a fixed size n of cusomers is no so realisic So we assume ha he oal size N of cusomers is disribued as a Poisson disribuion wih parameer (λ) Here (λ) represens he average oal size of cusomers over he inerval (, ) Le us compue Page 2 Special lecure/june 216

3 P {N(s) k} exacly: Noicing ha (11) becomes a condiional probabiliy here, we can compue P {N(s) k} nk P (N(s) k N n)p {N n} nk ( s ) k ( 1 s ) n k e λ (λ)n (n k)! e λ (λs) k nk (λ( s)) n k (n k)! λs (λs)k e So ha P {N(s) k} has a Poisson disribuion wih parameer λs Observe ha P {N(s) k} does no depend on and can be seen as a process couning up cusomers arriving by he ime s Thus, N(s) is called a Poisson process Problem 3 Le N(s), s, be a Poisson process as consruced as above (a) Show ha N() N(s) is disribued as a Poisson disribuion, and ha N() N(s) is independen of N(s) Then calculae E[(N() N(s))N(s)] (b) Using he formula Var(X) E[X 2 ] (E[X]) 2, calculae E[N(s) 2 ] Then find E[N(s)N()] Problem 4 Provided N() n, we can consider he ( arrival imes T (1), ), T (n) observed over T(k) (, ) (a) Find he condiional probabiliy G n (u) P u N() n on [, 1] (b) Show ha he cdf G n (u) has he bea densiy funcion wih (k, n + 1 k) Hin: In he above consrucion of Poisson process, we find ha he condiional probabiliy P (N(s) k N() n) has a binomial disribuion wih parameer (n, s ) for < s < Problem 5 Suppose ha cusomers arrive a a car dealership as a Poisson process wih average number λ of cusomers per hour, and ha a cusomer makes no decision (ie, no purchasing a car) wih probabiliy α >, independenly of oher cusomers Thus, none of he firs k cusomers purchases a car from he dealership wih probabily α k Wha is he probabiliy ha no car is sold by ime? Gamma disribued business hours Now we wan o change he assumpion on he arrival imes of cusomers Insead of he cusomers disribued uniformly over (, ), he waiing imes beween cusomers, he ime W 1 he firs cusomer arrives, he ime W 2 beween he firs cusomer and he second, and so on, are independenly and exponenially disribued wih parameer λ The successive waiing imes W 1, W 2,, are now iid exponenial random variables having he pdf { λe λx x ; f(x) x < Here he reciprocal λ 1 of he average number of cusomers par hour becomes he average waiing ime beween arrivals Thus, if he grocer likes o close his shop a he arrival of he (n + 1)h cusomer, such ime T n+1 is now a random variable Gamma disribuion The arrival ime of he kh cusomer T (k) W W k (13) Page 3 Special lecure/june 216

4 has a gamma disribuion wih parameer (k, λ) The pdf for gamma disribuion is given by where f() Γ(x) : λk Γ(k) k 1 e λ u x 1 e u du, x >, is called he gamma funcion When x k, i is simply Γ(k) We call he parameer k a shape parameer, because he change of k causes ha of he shape of densiy We call he parameer λ a scale parameer, because he change of λ merely rescales he densiy wihou changing is shape In paricular, he gamma disribuion wih k 1 becomes an exponenial disribuion Gamma disribuion and Poisson process The grocer has a mos k cusomers a he ime s if he (k + 1)h cusomer has no ye arrived This means, in erms of probabiliy, ha P {N(s) k} P {T (k+1) > s} for k, 1, (14) Here we claim by mahemaical inducion ha P {T (k+1) > s} k λs (λs)i e (15) i! i Clearly, P {N(s) } e λs P {T (1) > s} Now suppose ha (15) holds for k replaced by k 1 We have λe λx (λx) k P {T (k+1) > s} dx e λx (λx) k λe λx (λx) k 1 s + dx s s λs (λs)k k λs (λs)i e + P {T (k) > s} e, i! which has verified (15) Therefore, combining (14) and (15), we found ha P {N(s) k} has a Poisson disribuion wih parameer λs Gamma and bea disribuion Noice ha T (k) and T (n+1) T (k) W k W n+1 are independen and each disribued as a gamma disribuion wih respecive parameers (k, λ) and (n + 1 k, λ) And i is known ha i T (k) T (n+1) has a bea disribuion wih (k, n + 1 k) A doomed bus commuer (Feller, An Inroducion o Probabiliy Theory and Is Applicaions Vol 2, Secion 14, Waiing ime paradoxes) Timmy commues by he buses everyday The bus ransi auhoriy claims ha heir buses arrive in average every λ 1 1 minues Today he came o he bus sop a ime 66 (which is 5:pm measured from he sar of service a 6:am) How long is he expeced o wai for he nex bus? A sochasic model We can assume ha he successive waiing imes beween buses, say W 1, W 2,, are independen and exponenially disribued wih parameer λ Thus, he number Page 4 Special lecure/june 216

5 N() of buses having arrived by he ime has a Poisson disribuion wih parameer λ Le T (k) denoe he arrival ime of he kh bus; T (k) is again given by (13) Then his waiing ime V is formulaed as V : T (N()+1) Consider he even A : {V v} If N() occurs, hen A {N() } { < W 1 + v}, which gives P ( < W 1 + v) e λ e λ(+v) (1 e λv ) e λ (16) If N() k occurs for k 1, 2,, hen A {N() k} { T (k) < W k+1 + v T (k), T (k) < } Noice ha T (k) and W k+1 are independen, and herefore, hey have he join disribuion The waiing ime Now we can calculae f Wk+1,T (k) (x, y) λe λx λe λy (λy) k 1 P ( T (k) < W k+1 + v T (k), T (k) < ) f Wk+1,T (k) (x, y) dx dy {(x,y): y<x<+v y, y<} λe λy (λy) k 1 [e λ( y) e λ(+v y) ] dy (1 e λv ) e λ λ(λy) k 1 dy (1 e λv λ (λ)k ) e (17) By (16) (17), we obain P (V v) 1 e λv, where we have applied (λ) k k e λ Hence, V has an exponenial disribuion wih λ, and he expeced waiing ime E[V ] is λ 1 1 minues We can also observe ha P (V v, N() k) P (V v) P (N() k), and herefore, ha V and N() are independen Problem 6 Wha happens o he enire waiing ime U beween buses observed a he ime? This random variable is formulaed as U : T (N()+1) T (N()) By using an argumen similar o he above, verify ha { 1 (1 + λu)e λu if u < ; P (U u) 1 (1 + λ)e λu if u, and ha E[U ] (2 e λ )λ 1 This indicaes ha he waiing ime is much larger han he average λ 1 Hin: Le f (u) be he densiy funcion of U For he calculaion of expecaion we can use E[U ] uf (u) du f (u) du u dx dx f (u) du x P (U > x) dx Problem 7 The number of cusomers arriving a he grocer is a Poisson process wih average number λ of cusomers per hour Page 5 Special lecure/june 216

6 (a) Le T be he ime a which he firs cusomer arrives Wha is he disribuion of T? (b) Le > be fixed, and le T be he arrival ime of cusomer who shows up firs afer he ime Wha is he disribuion of T? Page 6 Special lecure/june 216

7 Summary of relaed disribuions Noe #1 Summary of relaed disribuions n Uniform on (, ) f(x) 1, < x < N(s) E[X] /2, Var(X) 2 /12 P {N(s) i} T (k) / T (1) s T (n) Binomial(n, p) ( ) n P {X i} p i (1 p) n i i E[X] np, Var(X) np(1 p) if p is small enough P {X i} e E[X] λ, Poisson(λ) P (N(s) k N() n) N() λ λi, i, 1, i! Var(X) λ Bea(k, n + 1 k) Γ(n + 1) f(x) Γ(k)Γ(n + 1 k) xk 1 (1 x) n k, < x < 1 E[X] k k(n + 1 k), Var(X) n + 1 (n + 1) 2 (n + 2) T (k) T (n+1) Gamma(n, λ) f(x) λe λx (λx) n 1, x Γ(n) E[X] n λ, Var(X) n λ 2 Exponenial(λ) f(x) λe λx, x E[X] 1 λ, Var(X) 1 λ 2 T (k) P {N(s) k} P {T (k+1) > s} N() n T (1) T (n) T (n+1) Page 7 Special lecure/june 216

8 Problem soluions Noe #1 Problem soluions Problem 1 Observe ha N is also a Poisson random variable wih parameer (α + β) Then we can calculae P (X k N n) P (X k, N n) P (N n) P (X k) P (Y n k) P (N n) ( ) ( ) k ( ) n k n α β k α + β α + β which is a binomial disribuion wih parameer Problem 2 (a) We can calculae ( n, α α+β P (X k, Y n k) P (N n) ) αk β βn k e α e (n k)! e (α+β) (α+β)n P (X j, Y k) P (X j, N j + k) P (X j N j + k) P (N j + k) ( j + k )p j (1 p) k λ λj+k (λp)j e e λ j (j + k)! j! (b) Using (a) we obain λ (λp)j P (X j) P (X j, Y k) e j! k (λ(1 p)) k k λp (λp)j e j! Similarly we can find ha Y has a Poisson disribuion wih parameer λ(1 p) (c) By applying (a) and (b), we have λp (λp)j P (X j, Y k) e j! which implies ha X and Y are independen λ(1 p) (λ(1 p))k e P (X j) P (Y k), Problem 3 (a) From he consrucion of Poisson process, have N() N(s) N N(s) In Problem 2 we found ha N N(s) has a Poisson disribuion wih parameer λ( s), and ha N(s) and N N(s) are independen Therefore, we have E[(N() N(s))N(s)] E[N() N(s)] E[N(s)] λ( s) λs (b) Since E[N(s) 2 ] λs + (λs) 2, we obain E[N(s)N()] λ 2 s + λs Problem 4 (a) Observe ha T (k) u if and only if N(u) k We obain n ( ) n G n (u) P (N(u) k N() n) u i (1 u) n i i (b) We can easily see ha g n (u) d du G n(u) ik (n k)!uk 1 (1 u) n k Γ(n + 1) Γ(k)Γ(n k + 1) uk 1 (1 u) n k Page 8 Special lecure/june 216

9 Problem soluions Noe #1 is he bea densiy funcion Problem 5 Le A be he even ha no car is sold Observe ha P (A N() k) α k Then we obain P (A) P (A N() k) P (N() k) k k α k λ (λ)k e Problem 6 Le B {U u} Case I: Suppose ha u < Then we have P (U u) P (B {N() k}) k P ( u T (k) <, T (k) < W k+1 u) k1 k1 u λe λy (λy) k 1 1 e λu λue λu Case II: Suppose ha u Then we have P (U u) P (B {N() k}) k P ( < W 1 u) + e λ e λu + k1 1 e λu λe λu Applying he formula in he hin, we obain E[U ] [e λu + λue λu ] du + u dy λe λx dx y e λ e λα e λ(1 α) P ( T (k) <, T (k) < W k+1 u) k1 λe λy (λy) k 1 u dy λe λx dx y [e λu + λe λu ] du (2 e λ )λ 1 Problem 7 (a) T T (1) W 1 has an exponenial disribuion wih λ (b) Using an exponenial random variable V wih λ, we can express T T (N()+1) + V, which has he densiy f(x) of shifed exponenial disribuion: { if x < ; f(x) λe λx if x Page 9 Special lecure/june 216