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Philipp Arras
whatisalikelihood
Commits
21336cf7
Commit
21336cf7
authored
Aug 24, 2018
by
Philipp Arras
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...
...
@@ -54,6 +54,9 @@
\maketitle
\VerbatimFootnotes
\section*
{
Why should I read this guide?
}
Let us define:
$
d
$
is the data vector and
$
s
$
is the physical field, you want to
learn from
$
d
$
in a Bayesian fashion.
Bayesian reconstruction algorithms may be viewed in terms of three basic
building blocks.
...
...
@@ -293,7 +296,9 @@ This test does the same as the test for the adjointness above.
So far, we have wrapped the derivative of the response in a
\texttt
{
LinearOperator
}
. The other thing to be done is to make the function
\texttt
{
R(s)
}
field-aware. Rewrite it such that it takes a field in signal space
as input and returns a field in data space.
as input and returns a field in data space. Make sure that all methods you have
implemented can deal with arbitrary sizes of the signal space. All pixel volumes
should be taken care of by you.
\section*
{
Example:
$
\gamma
$
-ray imaging
}
The information a
$
\gamma
$
-ray astronomer would provide to the algorithm (in the
...
...
@@ -314,5 +319,4 @@ Why is this already sufficient?
s
_
1
+
s
_
2
)
=
\alpha
R
(
s
_
1
)
+
R
(
s
_
2
)
$
.
}
Thus,
$
R'
=
R
$
and
$
R'
^
\dagger
=
R
^
\dagger
$
. All in all, we need an implementation for
$
R
$
and
$
R
^
\dagger
$
.
\end{itemize}
\end{document}
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