Commit 469aa08d authored by Gilles Kratzer's avatar Gilles Kratzer
Browse files

update website

parent 1d96d8da
Pipeline #2075 passed with stage
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</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">mcmcabn</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.1</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.2</span>
</span>
</div>
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</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">mcmcabn</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.1</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.2</span>
</span>
</div>
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<li>Imoto, S., Higuchi, T., Goto, T., Tashiro, K., Kuhara, S., and Miyano, S. (2003). Using Bayesian networks for estimating gene networks from microarrays and biological knowledge. In Proceedings of the European Conference on Computational Biology.</li>
<li>Goudie, R. J., and Mukherjee, S. (2016). A Gibbs Sampler for Learning DAGs. Journal of machine learning research: JMLR, 17(30), 1-39.</li>
<li>Kuipers, J. and Moffa, G. (2017). Partition MCMC for Inference on Acyclic Digraphs, Journal of the American Statistical Association, 112:517, 282-299, DOI: 10.1080/01621459.2015.1133426</li>
<li>Scutari, M. (2010). Learning Bayesian Networks with the bnlearn R Package. Journal of Statistical Software, 35(3), 1 - 22. <a href="doi:http://dx.doi.org/10.18637/jss.v035.i03" class="uri">doi:http://dx.doi.org/10.18637/jss.v035.i03</a>.</li>
<li>Scutari, M. (2010). Learning Bayesian Networks with the bnlearn R Package. Journal of Statistical Software, 35(3), 1 - 22. </li>
</ul>
</div>
</div>
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</button>
<span class="navbar-brand">
<a class="navbar-link" href="index.html">mcmcabn</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.1</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.2</span>
</span>
</div>
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......@@ -30,7 +30,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="index.html">mcmcabn</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.1</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.2</span>
</span>
</div>
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......@@ -60,7 +60,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">mcmcabn</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.1</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.2</span>
</span>
</div>
......@@ -128,6 +128,13 @@
<ul>
<li>mcmcabn(), query(), summary(), print() and plot() functions</li>
</ul>
</div>
<div id="mcmcabn-0-2" class="section level2">
<h2 class="hasAnchor">
<a href="#mcmcabn-0-2" class="anchor"></a>mcmcabn 0.2:</h2>
<ul>
<li>patch to make compatible with abn 2.0</li>
</ul>
</div>
</div>
......@@ -136,6 +143,7 @@
<h2>Contents</h2>
<ul class="nav nav-pills nav-stacked">
<li><a href="#mcmcabn-0-1">0.1</a></li>
<li><a href="#mcmcabn-0-2">0.2</a></li>
</ul>
</div>
</div>
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</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">mcmcabn</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.1</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.2</span>
</span>
</div>
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......@@ -63,7 +63,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">mcmcabn</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.1</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.2</span>
</span>
</div>
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......@@ -60,7 +60,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">mcmcabn</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.1</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.2</span>
</span>
</div>
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......@@ -63,7 +63,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">mcmcabn</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.1</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.2</span>
</span>
</div>
......@@ -207,7 +207,7 @@
<p>The procedure runs a structural Monte Carlo Markov Chain Model Choice (MC)^3 to find the most probable posterior network (DAG). The default algorithm is based on three MCMC move: edge addition, edge deletion and edge reversal. This algorithm is known as the (MC)^3. It is known to mix slowly and getting stuck in low probability regions. Indeed, changing of Markov equivalence region often requires multiple MCMC moves. Then large scale MCMC moves are implemented. Their relative frequency can be set by the user. The new edge reversal move (REV) from Grzegorczyk and Husmeier (2008) and the Markov blanket resampling (MBR) from Su and Borsuk (2016). The classical reversal move depends on the global configuration of the parents and children and fails to propose MCMC jumps that produce valid but very different DAGs in a unique move. The REV move sample globally a new set of parent. The MBR workaround applies the same idea but to the entire Markov blanket of a randomly chosen node.</p>
<p>The classical (MC)^3 is unbiased but inefficient in mixing, the two radical MCMC alternative move are known to massively accelerate mixing without introducing biases. But those move are computationally expensive. Then low frequencies are advised. The REV move is not necessarily ergotic , then it should not be used alone.</p>
<p>The parameter <code>start.dag</code> can be: "random", "hc" or user defined. If user select "random" then a random valid DAG is selected. The routine used favourise low density structure. If "hc" (for Hill-climber: search.heuristic then a DAG is selected using 100 different searches with 500 optimization steps. A user defined DAG can be provided. It should be a named square matrix containing only zeros and ones. The DAG should be valid (i.e. acyclic).</p>
<p>The parameter <code>start.dag</code> can be: "random", "hc" or user defined. If user select "random" then a random valid DAG is selected. The routine used favourise low density structure. If "hc" (for Hill-climber: searchHeuristic then a DAG is selected using 100 different searches with 500 optimization steps. A user defined DAG can be provided. It should be a named square matrix containing only zeros and ones. The DAG should be valid (i.e. acyclic).</p>
<p>The parameter <code>prior.choice</code> determines the prior used within each individual node for a given choice of parent combination. In Koivisto and Sood (2004) p.554 a form of prior is used which assumes that the prior probability for parent combinations comprising of the same number of parents are all equal. Specifically, that the prior probability for parent set G with cardinality |G| is proportional to 1/[n-1 choose |G|] where there are n total nodes. Note that this favours parent combinations with either very low or very high cardinality which may not be appropriate. This prior is used when <code>prior.choice=2</code>. When prior.choice=1 an uninformative prior is used where parent combinations of all cardinalities are equally likely. When <code>prior.choice=3</code> a user defined prior is used, defined by <code>prior.dag</code>. It is given by an adjacency matrix (squared and same size as number of nodes) where entries ranging from zero to one give the user prior belief. An hyper parameter defining the global user belief in the prior is given by <code>prior.lambda</code>.</p>
<p>MCMC sampler came with asymptotic statistical guarantees. Therefore it is highly advised to run multiple long enough chains. The burn in phase length (i.e throwing away first MCMC iterations) should be adequately chosen.</p>
<p>The argument <code>data.dists</code> must be a list with named arguments, one for each of the variables in <code>data.df</code>, where each entry is either "poisson","binomial", or "gaussian"</p>
......@@ -256,16 +256,8 @@
<span class='kw'>start.dag</span> <span class='kw'>=</span> <span class='st'>"random"</span>,
<span class='kw'>prob.rev</span> <span class='kw'>=</span> <span class='fl'>0.03</span>,
<span class='kw'>prob.mbr</span> <span class='kw'>=</span> <span class='fl'>0.03</span>,
<span class='kw'>prior.choice</span> <span class='kw'>=</span> <span class='fl'>2</span>)</div><div class='output co'>#&gt; [1] -12044.84
#&gt; [1] -12122.55
#&gt; [1] -12044.84
#&gt; [1] -11348.45
#&gt; [1] -11348.45
#&gt; [1] -11343.06
#&gt; [1] -11343.06
#&gt; [1] -11343.06
#&gt; [1] -11338.42
#&gt; [1] -12429.65</div><div class='input'>
<span class='kw'>prior.choice</span> <span class='kw'>=</span> <span class='fl'>2</span>)
<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/summary'>summary</a></span>(<span class='no'>mcmc.out.asia.small</span>)</div><div class='output co'>#&gt; MCMC summary:
#&gt; Number of Burn in steps: 0
#&gt; Number of MCMC steps: 100
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</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">mcmcabn</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.1</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.2</span>
</span>
</div>
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</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">mcmcabn</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.1</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.2</span>
</span>
</div>
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......@@ -63,7 +63,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">mcmcabn</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.1</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.2</span>
</span>
</div>
......
......@@ -63,7 +63,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">mcmcabn</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.1</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.2</span>
</span>
</div>
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