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Philipp Arras
whatisalikelihood
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a83b0169
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a83b0169
authored
Aug 24, 2018
by
Philipp Arras
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@@ -94,15 +94,16 @@ in a fashion which is compatible with our inference machinery called NIFTy.
temperature, flux, etc.) which are defined on a continuous space (like time,
space, frequency/energy of electromagnetic radiation). If you've got data,
which tells you something about such a field, keep on reading. If not, you may
use IFT and NIFTy as well but perhaps there are other methods available
to you
which
fit your needs better.
use IFT and NIFTy as well but perhaps there are other methods available
which
fit your needs better.
\item
The explanations below shall be pretty general in order to be applicable
to a variety of problems. However, there is some probability that your
\texttt
{
R
}
(will be defined below) is just a linear function. In this case
to a variety of problems. Therefore, the text might be difficult to understand
in its full generality. However, it is not unlikely that your
\texttt
{
R
}
(which will be defined below) is just a linear function. In this case
everything becomes very simple, since the derivative of a linear function is
just the function itself. Then only
\texttt
{
R(s)
}
and
\texttt
{
R
\_
adjoint(d)
}
need to be implemented. You do not need to understand what I
mean
already
at
this point. Just feel lucky and relax. You may relax anyways.
need to be implemented. You do not need to understand what I
'm saying
already
at
this point. Just feel lucky and relax. You may relax anyways.
\item
Read the full guide before you start to implement something.
\end{itemize}
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