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Copyright Tristan Aubrey-Jones April 2007.
function results = computePosteriorProbs(sample, prior, step, limit)
% size of sample
sz = size(sample);
if nargin == 3
limit = sz(1);
end
% init results
results = [];
% prior probabilities of models
%prior = [1/12; 5/12; 6/12];
% current log likelihoods
loglhood = [0; 0; 0];
% iterate through increasing sample sizes
for i = 1:limit
% compute prob of current event
p = probOfEvent(sample(i,1:sz(2)));
% compute product of likelihoods so far
loglhood = loglhood + log(p);
% if should take a reading
if mod(i,step) == 0
% get current likelihoods
lhoods = exp(loglhood);
% sum over hypotheses
hyp = sum(lhoods .* prior);
% compute posteriors
posts = (lhoods .* prior) ./ hyp;
% add to results
rsz = size(results);
results(rsz(1)+1, 1:3) = posts';
end
end
x = 1:step:limit;
plot(x, results);