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<h1>Module <a href="type_Gpr_cov_lin_ard.html">Gpr_cov_lin_ard</a></h1>
<pre><span class="keyword">module</span> Gpr_cov_lin_ard: <code class="code">sig</code> <a href="Gpr_cov_lin_ard.html">..</a> <code class="code">end</code></pre><div class="info module top">
<h6 id="6_CovarianceoflinearfunctionswithAutomaticRelevanceDetermination">Covariance of linear functions with Automatic Relevance Determination</h6><br>
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<br>
The covariance is defined as:
<p>
<code class="code">k(x, y) = x*inv(P)*y</code>
<p>
where P is a diagonal matrix containing ARD parameters ell_1^2,...,ell_D^2,
and D is the dimensionality of the input space.<br>
<pre><span class="keyword">module</span> <a href="Gpr_cov_lin_ard.Params.html">Params</a>: <code class="code">sig</code> <a href="Gpr_cov_lin_ard.Params.html">..</a> <code class="code">end</code></pre>
<pre><span class="keyword">module</span> <a href="Gpr_cov_lin_ard.Eval.html">Eval</a>: <code class="type">Eval</code><code class="type">
with type Kernel.params = Params.t</code><code class="type">
with type Inducing.t = mat</code><code class="type">
with type Input.t = vec</code><code class="type">
with type Inputs.t = mat</code></pre>
<pre><span class="keyword">module</span> <a href="Gpr_cov_lin_ard.Deriv.html">Deriv</a>: <code class="type">Deriv</code><code class="type">
with module Eval = Eval</code><code class="type">
with type Hyper.t = [ `Log_ell of int ]</code></pre></body></html>
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