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<li class="navelem"><a class="el" href="namespace_quant_lib.html">QuantLib</a></li><li class="navelem"><a class="el" href="class_quant_lib_1_1_polar_student_t_rng.html">PolarStudentTRng</a></li> </ul>
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<div class="title">PolarStudentTRng< URNG > Class Template Reference</div> </div>
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<p>Student t random number generator.
<a href="class_quant_lib_1_1_polar_student_t_rng.html#details">More...</a></p>
<p><code>#include <ql/experimental/math/polarstudenttrng.hpp></code></p>
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Public Types</h2></td></tr>
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typedef <a class="el" href="struct_quant_lib_1_1_sample.html">Sample</a>< <a class="el" href="group__types.html#ga4bdf4bfe76b9ffa6fa64c47d8bfa0c78">Real</a> > </td><td class="memItemRight" valign="bottom"><b>sample_type</b></td></tr>
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typedef URNG </td><td class="memItemRight" valign="bottom"><b>urng_type</b></td></tr>
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 </td><td class="memItemRight" valign="bottom"><b>PolarStudentTRng</b> (<a class="el" href="group__types.html#ga4bdf4bfe76b9ffa6fa64c47d8bfa0c78">Real</a> degFreedom, <a class="el" href="namespace_quant_lib.html#ae3df3f13e67885465f4e359b3bffa4e6">BigNatural</a> seed=0)</td></tr>
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 </td><td class="memItemRight" valign="bottom"><b>PolarStudentTRng</b> (<a class="el" href="group__types.html#ga4bdf4bfe76b9ffa6fa64c47d8bfa0c78">Real</a> degFreedom, const URNG &urng)</td></tr>
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<a class="el" href="struct_quant_lib_1_1_sample.html">sample_type</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="class_quant_lib_1_1_polar_student_t_rng.html#ab4a0f9431863aa7348f8b20f88580379">next</a> () const</td></tr>
<tr class="memdesc:ab4a0f9431863aa7348f8b20f88580379"><td class="mdescLeft"> </td><td class="mdescRight">returns a sample from a Student-t distribution <br /></td></tr>
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<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
<div class="textblock"><h3>template<class URNG><br />
class QuantLib::PolarStudentTRng< URNG ></h3>
<p>Student t random number generator. </p>
<p>Polar transformation based Student T random number generator. See "Polar Generation of Random Variates With the t-Distribution", Ralph W. Bailey, April 1994, in Mathematics of Computation, Vol 62-206 page 779. The one implemented here is a variant of this algorithm from "Random
Number Generation and Monte Carlo Methods", Springer, 2003, page 185. Using a uniform RNG on a [-1,1] support, the extra call to the uniform generator (used for the sign of the sample) is avoided.</p>
<p>Class RNG must implement the following interface: </p><div class="fragment"><div class="line">URNG::sample_type RNG::next() <span class="keyword">const</span>;</div>
</div><!-- fragment --><dl class="caveats"><dt><b><a class="el" href="caveats.html#_caveats000024">Warning:</a></b></dt><dd>do not use with a low-discrepancy sequence generator. </dd></dl>
<dl class="section examples"><dt>Examples</dt><dd><a class="el" href="_basket_losses_8cpp-example.html#_a17">BasketLosses.cpp</a>.</dd>
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