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<a name="Continuous-Distributions"></a>
<div class="header">
<p>
Next: <a href="Discrete-Distributions.html#Discrete-Distributions" accesskey="n" rel="next">Discrete Distributions</a>, Up: <a href="Statistical-Distribution-Functions.html#Statistical-Distribution-Functions" accesskey="u" rel="up">Statistical Distribution Functions</a> &nbsp; [<a href="index.html#SEC_Contents" title="Table of contents" rel="contents">Contents</a>][<a href="Function-Index.html#Function-Index" title="Index" rel="index">Index</a>]</p>
</div>
<hr>
<a name="Continuous-Distributions-1"></a>
<h4 class="subsubsection">7.7.10.1 Continuous Distributions</h4>

<p>The following continuous distributions are available:
</p>
<dl>
<dt><a name="index-PDF_002eBETA"></a>Function: <em></em> <strong>PDF.BETA</strong> <em>(<var>x</var>)</em></dt>
<dt><a name="index-CDF_002eBETA"></a>Function: <em></em> <strong>CDF.BETA</strong> <em>(<var>x</var>, <var>a</var>, <var>b</var>)</em></dt>
<dt><a name="index-IDF_002eBETA"></a>Function: <em></em> <strong>IDF.BETA</strong> <em>(<var>p</var>, <var>a</var>, <var>b</var>)</em></dt>
<dt><a name="index-RV_002eBETA"></a>Function: <em></em> <strong>RV.BETA</strong> <em>(<var>a</var>, <var>b</var>)</em></dt>
<dt><a name="index-NPDF_002eBETA"></a>Function: <em></em> <strong>NPDF.BETA</strong> <em>(<var>x</var>, <var>a</var>, <var>b</var>, <var>lambda</var>)</em></dt>
<dt><a name="index-NCDF_002eBETA"></a>Function: <em></em> <strong>NCDF.BETA</strong> <em>(<var>x</var>, <var>a</var>, <var>b</var>, <var>lambda</var>)</em></dt>
<dd><p>Beta distribution with shape parameters <var>a</var> and <var>b</var>.  The
noncentral distribution takes an additional parameter <var>lambda</var>.
Constraints: <var>a</var> &gt; 0, <var>b</var> &gt; 0, <var>lambda</var> &gt;= 0, 0 &lt;= <var>x</var>
&lt;= 1, 0 &lt;= <var>p</var> &lt;= 1.
</p></dd></dl>

<dl>
<dt><a name="index-PDF_002eBVNOR"></a>Function: <em></em> <strong>PDF.BVNOR</strong> <em>(<var>x0</var>, <var>x1</var>, <var>rho</var>)</em></dt>
<dt><a name="index-CDF_002eVBNOR"></a>Function: <em></em> <strong>CDF.VBNOR</strong> <em>(<var>x0</var>, <var>x1</var>, <var>rho</var>)</em></dt>
<dd><p>Bivariate normal distribution of two standard normal variables with
correlation coefficient <var>rho</var>.  Two variates <var>x0</var> and <var>x1</var>
must be provided.  Constraints: 0 &lt;= <var>rho</var> &lt;= 1, 0 &lt;= <var>p</var> &lt;= 1.
</p></dd></dl>

<dl>
<dt><a name="index-PDF_002eCAUCHY"></a>Function: <em></em> <strong>PDF.CAUCHY</strong> <em>(<var>x</var>, <var>a</var>, <var>b</var>)</em></dt>
<dt><a name="index-CDF_002eCAUCHY"></a>Function: <em></em> <strong>CDF.CAUCHY</strong> <em>(<var>x</var>, <var>a</var>, <var>b</var>)</em></dt>
<dt><a name="index-IDF_002eCAUCHY"></a>Function: <em></em> <strong>IDF.CAUCHY</strong> <em>(<var>p</var>, <var>a</var>, <var>b</var>)</em></dt>
<dt><a name="index-RV_002eCAUCHY"></a>Function: <em></em> <strong>RV.CAUCHY</strong> <em>(<var>a</var>, <var>b</var>)</em></dt>
<dd><p>Cauchy distribution with location parameter <var>a</var> and scale
parameter <var>b</var>.  Constraints: <var>b</var> &gt; 0, 0 &lt; <var>p</var> &lt; 1.
</p></dd></dl>

<dl>
<dt><a name="index-CDF_002eCHISQ"></a>Function: <em></em> <strong>CDF.CHISQ</strong> <em>(<var>x</var>, <var>df</var>)</em></dt>
<dt><a name="index-SIG_002eCHISQ"></a>Function: <em></em> <strong>SIG.CHISQ</strong> <em>(<var>x</var>, <var>df</var>)</em></dt>
<dt><a name="index-IDF_002eCHISQ"></a>Function: <em></em> <strong>IDF.CHISQ</strong> <em>(<var>p</var>, <var>df</var>)</em></dt>
<dt><a name="index-RV_002eCHISQ"></a>Function: <em></em> <strong>RV.CHISQ</strong> <em>(<var>df</var>)</em></dt>
<dt><a name="index-NCDF_002eCHISQ"></a>Function: <em></em> <strong>NCDF.CHISQ</strong> <em>(<var>x</var>, <var>df</var>, <var>lambda</var>)</em></dt>
<dd><p>Chi-squared distribution with <var>df</var> degrees of freedom.  The
noncentral distribution takes an additional parameter <var>lambda</var>.
Constraints: <var>df</var> &gt; 0, <var>lambda</var> &gt; 0, <var>x</var> &gt;= 0, 0 &lt;=
<var>p</var> &lt; 1.
</p></dd></dl>

<dl>
<dt><a name="index-PDF_002eEXP"></a>Function: <em></em> <strong>PDF.EXP</strong> <em>(<var>x</var>, <var>a</var>)</em></dt>
<dt><a name="index-CDF_002eEXP"></a>Function: <em></em> <strong>CDF.EXP</strong> <em>(<var>x</var>, <var>a</var>)</em></dt>
<dt><a name="index-IDF_002eEXP"></a>Function: <em></em> <strong>IDF.EXP</strong> <em>(<var>p</var>, <var>a</var>)</em></dt>
<dt><a name="index-RV_002eEXP"></a>Function: <em></em> <strong>RV.EXP</strong> <em>(<var>a</var>)</em></dt>
<dd><p>Exponential distribution with scale parameter <var>a</var>.  The inverse of
<var>a</var> represents the rate of decay.  Constraints: <var>a</var> &gt; 0,
<var>x</var> &gt;= 0, 0 &lt;= <var>p</var> &lt; 1.
</p></dd></dl>

<dl>
<dt><a name="index-PDF_002eXPOWER"></a>Function: <em></em> <strong>PDF.XPOWER</strong> <em>(<var>x</var>, <var>a</var>, <var>b</var>)</em></dt>
<dt><a name="index-RV_002eXPOWER"></a>Function: <em></em> <strong>RV.XPOWER</strong> <em>(<var>a</var>, <var>b</var>)</em></dt>
<dd><p>Exponential power distribution with positive scale parameter <var>a</var>
and nonnegative power parameter <var>b</var>.  Constraints: <var>a</var> &gt; 0,
<var>b</var> &gt;= 0, <var>x</var> &gt;= 0, 0 &lt;= <var>p</var> &lt;= 1.  This distribution is a
<small>PSPP</small> extension.
</p></dd></dl>

<dl>
<dt><a name="index-PDF_002eF"></a>Function: <em></em> <strong>PDF.F</strong> <em>(<var>x</var>, <var>df1</var>, <var>df2</var>)</em></dt>
<dt><a name="index-CDF_002eF"></a>Function: <em></em> <strong>CDF.F</strong> <em>(<var>x</var>, <var>df1</var>, <var>df2</var>)</em></dt>
<dt><a name="index-SIG_002eF"></a>Function: <em></em> <strong>SIG.F</strong> <em>(<var>x</var>, <var>df1</var>, <var>df2</var>)</em></dt>
<dt><a name="index-IDF_002eF"></a>Function: <em></em> <strong>IDF.F</strong> <em>(<var>p</var>, <var>df1</var>, <var>df2</var>)</em></dt>
<dt><a name="index-RV_002eF"></a>Function: <em></em> <strong>RV.F</strong> <em>(<var>df1</var>, <var>df2</var>)</em></dt>
<dd><p>F-distribution of two chi-squared deviates with <var>df1</var> and
<var>df2</var> degrees of freedom.  The noncentral distribution takes an
additional parameter <var>lambda</var>.  Constraints: <var>df1</var> &gt; 0,
<var>df2</var> &gt; 0, <var>lambda</var> &gt;= 0, <var>x</var> &gt;= 0, 0 &lt;= <var>p</var> &lt; 1.
</p></dd></dl>

<dl>
<dt><a name="index-PDF_002eGAMMA"></a>Function: <em></em> <strong>PDF.GAMMA</strong> <em>(<var>x</var>, <var>a</var>, <var>b</var>)</em></dt>
<dt><a name="index-CDF_002eGAMMA"></a>Function: <em></em> <strong>CDF.GAMMA</strong> <em>(<var>x</var>, <var>a</var>, <var>b</var>)</em></dt>
<dt><a name="index-IDF_002eGAMMA"></a>Function: <em></em> <strong>IDF.GAMMA</strong> <em>(<var>p</var>, <var>a</var>, <var>b</var>)</em></dt>
<dt><a name="index-RV_002eGAMMA"></a>Function: <em></em> <strong>RV.GAMMA</strong> <em>(<var>a</var>, <var>b</var>)</em></dt>
<dd><p>Gamma distribution with shape parameter <var>a</var> and scale parameter
<var>b</var>.  Constraints: <var>a</var> &gt; 0, <var>b</var> &gt; 0, <var>x</var> &gt;= 0, 0 &lt;=
<var>p</var> &lt; 1.
</p></dd></dl>



<dl>
<dt><a name="index-PDF_002eLANDAU"></a>Function: <em></em> <strong>PDF.LANDAU</strong> <em>(<var>x</var>)</em></dt>
<dt><a name="index-RV_002eLANDAU"></a>Function: <em></em> <strong>RV.LANDAU</strong> <em>()</em></dt>
<dd><p>Landau distribution.
</p></dd></dl>

<dl>
<dt><a name="index-PDF_002eLAPLACE"></a>Function: <em></em> <strong>PDF.LAPLACE</strong> <em>(<var>x</var>, <var>a</var>, <var>b</var>)</em></dt>
<dt><a name="index-CDF_002eLAPLACE"></a>Function: <em></em> <strong>CDF.LAPLACE</strong> <em>(<var>x</var>, <var>a</var>, <var>b</var>)</em></dt>
<dt><a name="index-IDF_002eLAPLACE"></a>Function: <em></em> <strong>IDF.LAPLACE</strong> <em>(<var>p</var>, <var>a</var>, <var>b</var>)</em></dt>
<dt><a name="index-RV_002eLAPLACE"></a>Function: <em></em> <strong>RV.LAPLACE</strong> <em>(<var>a</var>, <var>b</var>)</em></dt>
<dd><p>Laplace distribution with location parameter <var>a</var> and scale
parameter <var>b</var>.  Constraints: <var>b</var> &gt; 0, 0 &lt; <var>p</var> &lt; 1.
</p></dd></dl>

<dl>
<dt><a name="index-RV_002eLEVY"></a>Function: <em></em> <strong>RV.LEVY</strong> <em>(<var>c</var>, <var>alpha</var>)</em></dt>
<dd><p>Levy symmetric alpha-stable distribution with scale <var>c</var> and
exponent <var>alpha</var>.  Constraints: 0 &lt; <var>alpha</var> &lt;= 2.
</p></dd></dl>

<dl>
<dt><a name="index-RV_002eLVSKEW"></a>Function: <em></em> <strong>RV.LVSKEW</strong> <em>(<var>c</var>, <var>alpha</var>, <var>beta</var>)</em></dt>
<dd><p>Levy skew alpha-stable distribution with scale <var>c</var>, exponent
<var>alpha</var>, and skewness parameter <var>beta</var>.  Constraints: 0 &lt;
<var>alpha</var> &lt;= 2, -1 &lt;= <var>beta</var> &lt;= 1.
</p></dd></dl>

<dl>
<dt><a name="index-PDF_002eLOGISTIC"></a>Function: <em></em> <strong>PDF.LOGISTIC</strong> <em>(<var>x</var>, <var>a</var>, <var>b</var>)</em></dt>
<dt><a name="index-CDF_002eLOGISTIC"></a>Function: <em></em> <strong>CDF.LOGISTIC</strong> <em>(<var>x</var>, <var>a</var>, <var>b</var>)</em></dt>
<dt><a name="index-IDF_002eLOGISTIC"></a>Function: <em></em> <strong>IDF.LOGISTIC</strong> <em>(<var>p</var>, <var>a</var>, <var>b</var>)</em></dt>
<dt><a name="index-RV_002eLOGISTIC"></a>Function: <em></em> <strong>RV.LOGISTIC</strong> <em>(<var>a</var>, <var>b</var>)</em></dt>
<dd><p>Logistic distribution with location parameter <var>a</var> and scale
parameter <var>b</var>.  Constraints: <var>b</var> &gt; 0, 0 &lt; <var>p</var> &lt; 1.
</p></dd></dl>

<dl>
<dt><a name="index-PDF_002eLNORMAL"></a>Function: <em></em> <strong>PDF.LNORMAL</strong> <em>(<var>x</var>, <var>a</var>, <var>b</var>)</em></dt>
<dt><a name="index-CDF_002eLNORMAL"></a>Function: <em></em> <strong>CDF.LNORMAL</strong> <em>(<var>x</var>, <var>a</var>, <var>b</var>)</em></dt>
<dt><a name="index-IDF_002eLNORMAL"></a>Function: <em></em> <strong>IDF.LNORMAL</strong> <em>(<var>p</var>, <var>a</var>, <var>b</var>)</em></dt>
<dt><a name="index-RV_002eLNORMAL"></a>Function: <em></em> <strong>RV.LNORMAL</strong> <em>(<var>a</var>, <var>b</var>)</em></dt>
<dd><p>Lognormal distribution with parameters <var>a</var> and <var>b</var>.
Constraints: <var>a</var> &gt; 0, <var>b</var> &gt; 0, <var>x</var> &gt;= 0, 0 &lt;= <var>p</var> &lt; 1.
</p></dd></dl>

<dl>
<dt><a name="index-PDF_002eNORMAL"></a>Function: <em></em> <strong>PDF.NORMAL</strong> <em>(<var>x</var>, <var>mu</var>, <var>sigma</var>)</em></dt>
<dt><a name="index-CDF_002eNORMAL"></a>Function: <em></em> <strong>CDF.NORMAL</strong> <em>(<var>x</var>, <var>mu</var>, <var>sigma</var>)</em></dt>
<dt><a name="index-IDF_002eNORMAL"></a>Function: <em></em> <strong>IDF.NORMAL</strong> <em>(<var>p</var>, <var>mu</var>, <var>sigma</var>)</em></dt>
<dt><a name="index-RV_002eNORMAL"></a>Function: <em></em> <strong>RV.NORMAL</strong> <em>(<var>mu</var>, <var>sigma</var>)</em></dt>
<dd><p>Normal distribution with mean <var>mu</var> and standard deviation
<var>sigma</var>.  Constraints: <var>b</var> &gt; 0, 0 &lt; <var>p</var> &lt; 1.  Three
additional functions are available as shorthand:
</p>
<dl>
<dt><a name="index-CDFNORM"></a>Function: <em></em> <strong>CDFNORM</strong> <em>(<var>x</var>)</em></dt>
<dd><p>Equivalent to CDF.NORMAL(<var>x</var>, 0, 1).
</p></dd></dl>

<dl>
<dt><a name="index-PROBIT"></a>Function: <em></em> <strong>PROBIT</strong> <em>(<var>p</var>)</em></dt>
<dd><p>Equivalent to IDF.NORMAL(<var>p</var>, 0, 1).
</p></dd></dl>

<dl>
<dt><a name="index-NORMAL"></a>Function: <em></em> <strong>NORMAL</strong> <em>(<var>sigma</var>)</em></dt>
<dd><p>Equivalent to RV.NORMAL(0, <var>sigma</var>).
</p></dd></dl>
</dd></dl>

<dl>
<dt><a name="index-PDF_002eNTAIL"></a>Function: <em></em> <strong>PDF.NTAIL</strong> <em>(<var>x</var>, <var>a</var>, <var>sigma</var>)</em></dt>
<dt><a name="index-RV_002eNTAIL"></a>Function: <em></em> <strong>RV.NTAIL</strong> <em>(<var>a</var>, <var>sigma</var>)</em></dt>
<dd><p>Normal tail distribution with lower limit <var>a</var> and standard
deviation <var>sigma</var>.  This distribution is a <small>PSPP</small> extension.
Constraints: <var>a</var> &gt; 0, <var>x</var> &gt; <var>a</var>, 0 &lt; <var>p</var> &lt; 1.
</p></dd></dl>

<dl>
<dt><a name="index-PDF_002ePARETO"></a>Function: <em></em> <strong>PDF.PARETO</strong> <em>(<var>x</var>, <var>a</var>, <var>b</var>)</em></dt>
<dt><a name="index-CDF_002ePARETO"></a>Function: <em></em> <strong>CDF.PARETO</strong> <em>(<var>x</var>, <var>a</var>, <var>b</var>)</em></dt>
<dt><a name="index-IDF_002ePARETO"></a>Function: <em></em> <strong>IDF.PARETO</strong> <em>(<var>p</var>, <var>a</var>, <var>b</var>)</em></dt>
<dt><a name="index-RV_002ePARETO"></a>Function: <em></em> <strong>RV.PARETO</strong> <em>(<var>a</var>, <var>b</var>)</em></dt>
<dd><p>Pareto distribution with threshold parameter <var>a</var> and shape
parameter <var>b</var>.  Constraints: <var>a</var> &gt; 0, <var>b</var> &gt; 0, <var>x</var> &gt;=
<var>a</var>, 0 &lt;= <var>p</var> &lt; 1.
</p></dd></dl>

<dl>
<dt><a name="index-PDF_002eRAYLEIGH"></a>Function: <em></em> <strong>PDF.RAYLEIGH</strong> <em>(<var>x</var>, <var>sigma</var>)</em></dt>
<dt><a name="index-CDF_002eRAYLEIGH"></a>Function: <em></em> <strong>CDF.RAYLEIGH</strong> <em>(<var>x</var>, <var>sigma</var>)</em></dt>
<dt><a name="index-IDF_002eRAYLEIGH"></a>Function: <em></em> <strong>IDF.RAYLEIGH</strong> <em>(<var>p</var>, <var>sigma</var>)</em></dt>
<dt><a name="index-RV_002eRAYLEIGH"></a>Function: <em></em> <strong>RV.RAYLEIGH</strong> <em>(<var>sigma</var>)</em></dt>
<dd><p>Rayleigh distribution with scale parameter <var>sigma</var>.  This
distribution is a <small>PSPP</small> extension.  Constraints: <var>sigma</var> &gt; 0,
<var>x</var> &gt; 0.
</p></dd></dl>

<dl>
<dt><a name="index-PDF_002eRTAIL"></a>Function: <em></em> <strong>PDF.RTAIL</strong> <em>(<var>x</var>, <var>a</var>, <var>sigma</var>)</em></dt>
<dt><a name="index-RV_002eRTAIL"></a>Function: <em></em> <strong>RV.RTAIL</strong> <em>(<var>a</var>, <var>sigma</var>)</em></dt>
<dd><p>Rayleigh tail distribution with lower limit <var>a</var> and scale
parameter <var>sigma</var>.  This distribution is a <small>PSPP</small> extension.
Constraints: <var>a</var> &gt; 0, <var>sigma</var> &gt; 0, <var>x</var> &gt; <var>a</var>.
</p></dd></dl>



<dl>
<dt><a name="index-PDF_002eT"></a>Function: <em></em> <strong>PDF.T</strong> <em>(<var>x</var>, <var>df</var>)</em></dt>
<dt><a name="index-CDF_002eT"></a>Function: <em></em> <strong>CDF.T</strong> <em>(<var>x</var>, <var>df</var>)</em></dt>
<dt><a name="index-IDF_002eT"></a>Function: <em></em> <strong>IDF.T</strong> <em>(<var>p</var>, <var>df</var>)</em></dt>
<dt><a name="index-RV_002eT"></a>Function: <em></em> <strong>RV.T</strong> <em>(<var>df</var>)</em></dt>
<dd><p>T-distribution with <var>df</var> degrees of freedom.  The noncentral
distribution takes an additional parameter <var>lambda</var>.  Constraints:
<var>df</var> &gt; 0, 0 &lt; <var>p</var> &lt; 1.
</p></dd></dl>

<dl>
<dt><a name="index-PDF_002eT1G"></a>Function: <em></em> <strong>PDF.T1G</strong> <em>(<var>x</var>, <var>a</var>, <var>b</var>)</em></dt>
<dt><a name="index-CDF_002eT1G"></a>Function: <em></em> <strong>CDF.T1G</strong> <em>(<var>x</var>, <var>a</var>, <var>b</var>)</em></dt>
<dt><a name="index-IDF_002eT1G"></a>Function: <em></em> <strong>IDF.T1G</strong> <em>(<var>p</var>, <var>a</var>, <var>b</var>)</em></dt>
<dd><p>Type-1 Gumbel distribution with parameters <var>a</var> and <var>b</var>.  This
distribution is a <small>PSPP</small> extension.  Constraints: 0 &lt; <var>p</var> &lt; 1.
</p></dd></dl>

<dl>
<dt><a name="index-PDF_002eT2G"></a>Function: <em></em> <strong>PDF.T2G</strong> <em>(<var>x</var>, <var>a</var>, <var>b</var>)</em></dt>
<dt><a name="index-CDF_002eT2G"></a>Function: <em></em> <strong>CDF.T2G</strong> <em>(<var>x</var>, <var>a</var>, <var>b</var>)</em></dt>
<dt><a name="index-IDF_002eT2G"></a>Function: <em></em> <strong>IDF.T2G</strong> <em>(<var>p</var>, <var>a</var>, <var>b</var>)</em></dt>
<dd><p>Type-2 Gumbel distribution with parameters <var>a</var> and <var>b</var>.  This
distribution is a <small>PSPP</small> extension.  Constraints: <var>x</var> &gt; 0, 0 &lt;
<var>p</var> &lt; 1.
</p></dd></dl>

<dl>
<dt><a name="index-PDF_002eUNIFORM"></a>Function: <em></em> <strong>PDF.UNIFORM</strong> <em>(<var>x</var>, <var>a</var>, <var>b</var>)</em></dt>
<dt><a name="index-CDF_002eUNIFORM"></a>Function: <em></em> <strong>CDF.UNIFORM</strong> <em>(<var>x</var>, <var>a</var>, <var>b</var>)</em></dt>
<dt><a name="index-IDF_002eUNIFORM"></a>Function: <em></em> <strong>IDF.UNIFORM</strong> <em>(<var>p</var>, <var>a</var>, <var>b</var>)</em></dt>
<dt><a name="index-RV_002eUNIFORM"></a>Function: <em></em> <strong>RV.UNIFORM</strong> <em>(<var>a</var>, <var>b</var>)</em></dt>
<dd><p>Uniform distribution with parameters <var>a</var> and <var>b</var>.
Constraints: <var>a</var> &lt;= <var>x</var> &lt;= <var>b</var>, 0 &lt;= <var>p</var> &lt;= 1.  An
additional function is available as shorthand:
</p>
<dl>
<dt><a name="index-UNIFORM"></a>Function: <em></em> <strong>UNIFORM</strong> <em>(<var>b</var>)</em></dt>
<dd><p>Equivalent to RV.UNIFORM(0, <var>b</var>).
</p></dd></dl>
</dd></dl>

<dl>
<dt><a name="index-PDF_002eWEIBULL"></a>Function: <em></em> <strong>PDF.WEIBULL</strong> <em>(<var>x</var>, <var>a</var>, <var>b</var>)</em></dt>
<dt><a name="index-CDF_002eWEIBULL"></a>Function: <em></em> <strong>CDF.WEIBULL</strong> <em>(<var>x</var>, <var>a</var>, <var>b</var>)</em></dt>
<dt><a name="index-IDF_002eWEIBULL"></a>Function: <em></em> <strong>IDF.WEIBULL</strong> <em>(<var>p</var>, <var>a</var>, <var>b</var>)</em></dt>
<dt><a name="index-RV_002eWEIBULL"></a>Function: <em></em> <strong>RV.WEIBULL</strong> <em>(<var>a</var>, <var>b</var>)</em></dt>
<dd><p>Weibull distribution with parameters <var>a</var> and <var>b</var>.
Constraints: <var>a</var> &gt; 0, <var>b</var> &gt; 0, <var>x</var> &gt;= 0, 0 &lt;= <var>p</var> &lt; 1.
</p></dd></dl>

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