Commit 8a31dbbb authored by Gilles Kratzer's avatar Gilles Kratzer
Browse files

gk review. new plot + text

parent 5ba000de
......@@ -5,7 +5,7 @@
\citation{b5}
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\citation{b6}
\citation{b6}
\bibcite{b1}{1}
......@@ -20,6 +20,6 @@
\@writefile{toc}{\contentsline {section}{\numberline {IV}Discussion}{2}\protected@file@percent }
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\@writefile{toc}{\contentsline {section}{References}{2}\protected@file@percent }
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......@@ -66,7 +66,7 @@ parametric or non parametric approaches prune the selected structure and usually
theory-compatible structure is reported. Albeit being popular and accepted, this process makes very
crude choices regarding the possible connections in the model and diminish the range of possible
interpretation. Indeed, an arc is either present of absent. This is exceptionally rudimentary taken
into account the massive a priori possibilities of structures.
into account the massive number of a priori networks.
%In contrast to other fields, the acceptance rate \rf{which?} of BN modelling, represented by directed acyclic
%graphs (DAGs), is relatively low compared to more traditional statistical approach within the
......@@ -81,7 +81,7 @@ into account the massive a priori possibilities of structures.
Classically in statistics any relationship between variables is given with an estimate of their data
support. A possible counterpart in BN modelling, would be the so called Monte Carlo Markov chain
model choice (MC$^3$) which sample over all possible structures and move from structures to
model choice ((MC)$^3$) which sample over all possible structures and move from structures to
structure according to their support by the data. Indeed, the posterior probability of any
structural feature could be obtained from a Markov chain sample computed from graph structure by
\begin{equation}
......@@ -102,10 +102,10 @@ importance in systems epidemiology where the data are usually scarce and, as a r
depends heavily on the prior choice. Again, from a data analysis point of view this would weaken the
analysis and render it not fully adapted. Two structurally transparent workarounds have been
proposed: the new edge reversal move \cite{b4} and the Markov blanket resampling \cite{b5}. The
former advocates to make a reversal move in resampling the parent set. However, the classical
former advocates to make a reversal move in resampling the parent set. Indeed, the classical
reversal move depends on the global configuration of the parents and children, and then fails to
propose MCMC jumps that produce valid but very different DAGs in a unique move. In the latter the
same idea is applied but to the entire Markov blanket of a randomly chosen node.
same idea is applied but to the entire Markov blanket of a randomly chosen node. Single edge and both structural methods were implemented in \textsc{mcmcabn}.
\section{Simulation Study}
......
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