#C10.txt: Maple code for Lecture 10 Help10:=proc(): print(` SAc(f,x,K), LD(L) ,PlotDist(f,x,K) , OneTrial(p,n)`): print(`ManyTrials(p,n,K,x)`): end: #Taken from Blair Seidler's homework (who adapted a previous #code from Dr. Z.) #SAc(f,x,K): The SA(X,x,K) of the rv X such whose Pgf is f of x SAc:=proc(f,x,K) local i,mu,sig2,M,f1: if subs(x=1,f)=0 then RETURN(0): fi: f1:=f/subs(x=1,f): mu:=subs(x=1,diff(f1,x)): f1:=f1/x^mu: f1:=x*diff(f1,x): f1:=x*diff(f1,x): sig2:=subs(x=1,f1): M:=[mu,sig2]: if sig2=0 then RETURN(M): fi: for i from 3 to K do f1:=x*diff(f1,x): # M:=[op(M), evalf(subs(x=1,f1)/sig2^(i/2))]: M:=[op(M), subs(x=1,f1)/sig2^(i/2)]: od: M: end: #LD(L): Inputs a list of positive integers L (of n:=nops(L) members) #outputs an integer i from 1 to n with the prob. of i being #proportional to L[i] #For example LD([1,2,3]) should output 1 with prob. 1/6 #output 2 with prob. 1/3 #output 3 with prob. 3/6=1/2 LD:=proc(L) local n,i,su,r: n:=nops(L): r:=rand(1..convert(L,`+`))(): su:=0: for i from 1 to n do su:=su+L[i]: if r<=su then RETURN(i): fi: od: end: #PlotDist(f,x,K): Inputs a prob. generating function of #some r.v. X outputs a graph of its SCALED distribution #A graph of the pdf of (X-mu)/sig. It draws it #from -K*sig to K*sig PlotDist:=proc(f,x,K) local i,mu,sig,M,f1: if subs(x=1,f)=0 then RETURN(0): fi: f1:=f/subs(x=1,f): mu:=subs(x=1,diff(f1,x)): f1:=f1/x^mu: f1:=x*diff(f1,x): f1:=x*diff(f1,x): sig:=sqrt(subs(x=1,f1)): if sig=0 then RETURN(FAIL): fi: #corrected after class, following Blair's suggestion plot([seq([(i-mu)/sig,coeff(f,x,i)/subs(x=1,f)*sig],i=trunc(mu-K*sig)..trunc(mu+K*sig))]): end: #OneTrial(p,n): The outcome of ONE trial of tossing a coin n times #where Pr(Head)=p. Returns the number of Heads #p=1/3 OneTrial:=proc(p,n) local L,p1: p1:=convert(p,rational): L:=[denom(p1)-numer(p1), numer(p1)]: add(LD(L)-1,i=1..n): end: #ManyTrials(p,n,K,x): the EMPIRICAL PGF in the variable x, of repeating K times #OneTrial(p,n) ManyTrials:=proc(p,n,K,x) local i: add( x^OneTrial(p,n) , i=1..K)/K: end: