An Introduction to Stochastic Processes: [X]=e239b - File Exchange

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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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from An Introduction to Stochastic Processes by Edward Kao
Companion Software

[X]=e239b

function [X]=e239b
%
%   M(t)/m/1/4  Queue
%
%   This version is one to use - 3/2/94
%
t0=0; tf=8; x0=zeros(1,5); x0(1,1)=1; 
[t,x]=ode45('e239a',t0,tf,x0);
[n]=length(t); X=zeros(n,2); X(:,1)=t; X(:,2)=x(:,5);

Highlights from An Introduction to Stochastic Processes

Highlights from
An Introduction to Stochastic Processes