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ocaml-gpr-devel-1.2.1-2.mga6.x86_64.rpm

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<h1>Module <a href="type_Gpr_interfaces.Sigs.Eval.Sampler.html">Gpr_interfaces.Sigs.Eval.Sampler</a></h1>

<pre><span class="keyword">module</span> Sampler: <code class="code">sig</code> <a href="Gpr_interfaces.Sigs.Eval.Sampler.html">..</a> <code class="code">end</code></pre><div class="info module top">
Module for sampling single points from the posterior distribution<br>
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<pre><span id="TYPEt"><span class="keyword">type</span> <code class="type"></code>t</span> </pre>
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Type of sampler<br>
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<pre><span id="VALcalc"><span class="keyword">val</span> calc</span> : <code class="type">?predictive:bool -><br>       <a href="Gpr_interfaces.Sigs.Eval.Mean.html#TYPEt">Gpr_interfaces.Sigs.Eval.Mean.t</a> -><br>       <a href="Gpr_interfaces.Sigs.Eval.Variance.html#TYPEt">Gpr_interfaces.Sigs.Eval.Variance.t</a> -> <a href="Gpr_interfaces.Sigs.Eval.Sampler.html#TYPEt">t</a></code></pre><div class="info ">
<code class="code">calc ?predictive mean variance</code><br>
<b>Returns</b> sampler given <code class="code">mean</code> and
          <code class="code">variance</code>.  If <code class="code">predictive</code> is true, the samples will be noisy.<br>
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<pre><span id="VALsample"><span class="keyword">val</span> sample</span> : <code class="type">?rng:Gsl.Rng.t -> <a href="Gpr_interfaces.Sigs.Eval.Sampler.html#TYPEt">t</a> -> float</code></pre><div class="info ">
<code class="code">sample ?rng sampler</code><br>
<b>Returns</b> a sample from the posterior distribution
          given <code class="code">sampler</code> and GSL random number generator <code class="code">rng</code>.<br>
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<div class="param_info"><code class="code">rng</code> : default = GSL default</div>

<pre><span id="VALsamples"><span class="keyword">val</span> samples</span> : <code class="type">?rng:Gsl.Rng.t -> <a href="Gpr_interfaces.Sigs.Eval.Sampler.html#TYPEt">t</a> -> n:int -> Lacaml.D.vec</code></pre><div class="info ">
<code class="code">samples ?rng sampler ~n</code><br>
<b>Returns</b> <code class="code">n</code> samples from the posterior
          distribution given <code class="code">sampler</code>.<br>
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<div class="param_info"><code class="code">rng</code> : default = GSL default</div>
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