Commit 8428f7f4 authored by Gilles Kratzer's avatar Gilles Kratzer
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update tarbal+ pkgdown

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......@@ -34,7 +34,8 @@ The three main problems addressed by this R package are:
- selecting the most probable structure based on a cache of pre-computed scores.
- controlling for overfitting.
- sampling the landscape of high scoring structures.
The latter could be very useful in an applied perspective to avoid reducing the richeness of Bayesian network modelling to only one structure. Indeed, it allows user to quantify the marginal impact of relationships of interest by marginalising out over structures or nuisance dependencies. Structural MCMC seems a very elegant and natural way to estimate the true marginal impact, so one can determine if it’s magnitude is big enough to consider as a worthwhile intervention.
The latter could be very useful in an applied perspective to avoid reducing the richeness of Bayesian network modelling to only **one** structure. Indeed, it allows user to quantify the marginal impact of relationships of interest by marginalising out over structures or nuisance dependencies. Structural MCMC seems a very elegant and natural way to estimate the true marginal impact, so one can determine if it’s magnitude is big enough to consider as a worthwhile intervention.
## Description
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title: mcmcabn
url:
url: https://www.math.uzh.ch/pages/mcmcabn/
authors:
Gilles Kratzer:
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......@@ -105,8 +105,9 @@
<ul>
<li>selecting the most probable structure based on a cache of pre-computed scores.</li>
<li>controlling for overfitting.</li>
<li>sampling the landscape of high scoring structures. The latter could be very useful in an applied perspective to avoid reducing the richeness of Bayesian network modelling to only one structure. Indeed, it allows user to quantify the marginal impact of relationships of interest by marginalising out over structures or nuisance dependencies. Structural MCMC seems a very elegant and natural way to estimate the true marginal impact, so one can determine if it’s magnitude is big enough to consider as a worthwhile intervention.</li>
<li>sampling the landscape of high scoring structures.</li>
</ul>
<p>The latter could be very useful in an applied perspective to avoid reducing the richeness of Bayesian network modelling to only <strong>one</strong> structure. Indeed, it allows user to quantify the marginal impact of relationships of interest by marginalising out over structures or nuisance dependencies. Structural MCMC seems a very elegant and natural way to estimate the true marginal impact, so one can determine if it’s magnitude is big enough to consider as a worthwhile intervention.</p>
</div>
<div id="description" class="section level2">
<h2 class="hasAnchor">
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