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The computation og the error term dy2 in VBA_Iphi does not exclude the points that should be excluded according to isYout. This causes bugs when NaNs are present in the data and, more problematically, can silently bias the inference.
VBA_Iphi_extended does not have the problem.
The text was updated successfully, but these errors were encountered:
Normally, there is a bit of code in VBA_check that ensures that the data precision matrices priors.iQy{t} are zeroed at the corresponding data points:
% ensure excluded data consistency
if ~options.binomial
for t=1:dim.n_t
diQ = diag(priors.iQy{t})._~options.isYout(:,t);
options.isYout(:,t) = ~diQ;
priors.iQy{t} = diag(diQ)_priors.iQy{t}*diag(diQ);
end
end
In turn, this ensures that dy2 is removing the excluded data points (cf., in VBA_IPhi):
dy2 = dy2 + dy(:,t)'_iQy{t}_dy(:,t);
The problem is that 0*NaN gives a NaN, so if the data are effectively excluded, a NaN in the data will generate a nan dy2 even if it is excluded according to isYout.
So I retract my dramatic statement about potential silent problems, but I am sure that NaNs break the inversion!
The computation og the error term dy2 in VBA_Iphi does not exclude the points that should be excluded according to isYout. This causes bugs when NaNs are present in the data and, more problematically, can silently bias the inference.
VBA_Iphi_extended does not have the problem.
The text was updated successfully, but these errors were encountered: