poissrp

# poissrp - maxt = max(t); % Get sizes Nt = length(t); Np =...

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function [X,t] = poissrp(N,t,l) % [X,t] = poissrp(N,t,l) % % N : Number of sample paths to generate % t : Vector of time points at which to generate samples % l : Arrival rate. OPTIONAL. Default l=1. % % X : Matrix of process sample paths. Each row is a sample path, each column % is a different time point. % % X(t) = Poisson Random process with rate l % % n % (lt) -lt % f_{X(t)}(n) = ---- e % n! % W. C. Karl if min(size(t)) > 1 error('t must be a vector of time point') end; if max(size(N))>1 error('N must be a scalar number of sample paths to generate') end; if nargin<3 l = 1; end; t = t(:)'; % Make a row vector
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Unformatted text preview: maxt = max(t); % Get sizes Nt = length(t); Np = N; % Generate exponential interarrival times till all samples exceed maxt % Tau contains the set of exponential interarrival times for each experiment % -- one per row Tau = randexp(Np,5,l); while min(sum(Tau')) < maxt Tau = [Tau,randexp(Np,1,l)]; end; % Generate Waiting times from interarrival times W = cumsum(Tau')'; % Generate Process values at the desired time points by summing number of % arrivals prior to each time X = zeros(Np,Nt); for i = 1:Nt X(:,i) = sum((W<t(i))')'; end;...
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## This note was uploaded on 09/29/2009 for the course EC 505 taught by Professor Karl during the Fall '04 term at BU.

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