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Highlights from
An Introduction to Stochastic Processes

• E645_a
• [A]=phaseak(a,T,b,S)
• [D]=e332c % Compute the renewal function with U(0,1) interarrival time
• [D]=e713
• [F,g]=c3_wibo1(alpha,beta,t)
• [F]=fpss_pro(P,m,n) % For the Markov chain with transition matrix P,
• [H]=e589
• [PC]=mc_canon(P,g)
• [P]=e411_dat
• [P]=e413_dat(m,s,S) % Find the transition matrix for the (s,S) inventory example
• [P]=e422_dat(m) % Transition matrix for Example 4.2.2
• [P]=e426_dat
• [P]=e441_dat
• [Q,f]=mc_canop(P) % For a periodic irreducible Markov chain with transition probability matrix
• [R]=e621
• [R]=gph1_sra(A)
• [T,t]=e331 % Simulating mean total life
• [X]=c3_mt_f(F,g,t)
• [X]=e2311
• [X]=e239b
• [X]=e446 % Example 4.4.6 - the coupon collection example
• [X]=e523 % The Emergency Service Example
• [X]=e631
• [X]=e632
• [X]=e633
• [X]=e645_c % The main calling program for Example 6.4.5
• [X]=e649
• [X]=e711 % Simulate a standard Weiner process
• [X]=e712_c
• [X]=e721
• [X]=e722 % Find virtual delay distribution of PH/PH/1 queue
• [X]=e724a
• [Z]=e522
• [a,T]=e721a
• [b,S]=e721b
• [f]=invt_laq(input,t) % Numerical Inversion of Laplace transform
• [g]=mc_equca(P,po) % Find the equivalent classes a Markov Chain
• [gcd]=mc_GCD(a,b)
• [lm]=e2311b(t) % Generate the arrival rate for Example 2.3.11
• [lm]=e236(t) % Generate the arrival rate for Example 2.3.6
• [p]=c4_pos(m) % It produces a Poisson mass function with mean m
• [p]=mc_limsr(Q) % Find the startionary probability vector pi of an irreducible,
• [q,v]=mc_perdo(P) % The algorithm is from "Periods of Connected Networks and Powers of
• [tx,qx]=invt_lap(t) % Numerical Inversion of Laplace transform
• [u1,v]=phasemv(a,T)
• [w]=binomial(n,m)
• [xdot]=e2311d(t,x)
• [xdot]=e239a(t,x)
• [y]=e129pgf(z)
• [y]=e649_a(x) global lm1 lm2 bt1 bt2 b
• [y]=e649_b(x) global lm1 lm2 bt1 bt2 b
• [y]=e724b(x)
• [y]=e724c(x)
• [z]=c4_cpos(m,x)
• [z]=e135(x,y)
• [z]=e332b(x,y)
• [z]=e332g(x,w)
• [z]=e451_run
• [z]=e645_b(x,y)
• e435
• e441_run
• e443
• e541
• e571
• e573
• e739
• invt_pgf % Inverting PGF to the time domain
• out=e332d(y,ymin,ymax)
• out=e332e(variable)
• out=e332f(fun,outvar,xmin,xma...
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