getProbAggr.m

function [Pnir,logPnir] = getProbAggr(self, ist)
% [PNIR,LOGPNIR] = GETPROBAGGR(IST)
%
% Probability of a SPECIFIC per-class job distribution at a station.
% Returns P(n1 jobs of class 1, n2 jobs of class 2, ...) for current state.
%
% Compare with getProbMarg: returns total queue-length distribution,
% i.e., P(n total jobs) summed over all class combinations.
%
% Input:
%   ist - Station index
%
% Output:
%   Pnir    - Scalar probability in [0,1]
%   logPnir - Log probability for numerical stability
 
if nargin<2 %~exist('ist','var')
    line_error(mfilename,'getProbAggr requires to pass a parameter the station of interest.');
end
if ist > sn.nstations
    line_error(mfilename,'Station number exceeds the number of stations in the model.');
end
if isempty(self.result)
    self.run;
end
Q = self.result.Avg.Q;
sn = self.getStruct;
N = sn.njobs;
if all(isfinite(N))
    state = sn.state{sn.stationToStateful(ist)};
    [~, nir, ~, ~] = State.toMarginal(sn, ist, state);
    % Binomial approximation with mean fitted to queue-lengths.
    % Rainer Schmidt, "An approximate MVA ...", PEVA 29:245-254, 1997.
    logPnir = 0;
    for r=1:size(nir,2)
        logPnir = logPnir + nchoosekln(N(r),nir(r));
        logPnir = logPnir + nir(r)*log(Q(ist,r)/N(r));
        logPnir = logPnir + (N(r)-nir(r))*log(1-Q(ist,r)/N(r));
    end
    Pnir = real(exp(logPnir));
else
    line_error(mfilename,'getProbAggr not yet implemented for models with open classes.');
end
end