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4 changes: 2 additions & 2 deletions docs/betacdf.html
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<div class="collapse navbar-collapse" id="navbarNav">
<ul class="navbar-nav">
<li class="nav-item">
<a class="nav-link" href="index.html#Distributions">
<a class="nav-link" href="index.html#Distribution Functions">
<i class="fas fa-list-alt"></i>
Distributions
Distribution Functions
</a>
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4 changes: 2 additions & 2 deletions docs/betainv.html
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<div class="collapse navbar-collapse" id="navbarNav">
<ul class="navbar-nav">
<li class="nav-item">
<a class="nav-link" href="index.html#Distributions">
<a class="nav-link" href="index.html#Distribution Functions">
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Distributions
Distribution Functions
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4 changes: 2 additions & 2 deletions docs/betapdf.html
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<div class="collapse navbar-collapse" id="navbarNav">
<ul class="navbar-nav">
<li class="nav-item">
<a class="nav-link" href="index.html#Distributions">
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4 changes: 2 additions & 2 deletions docs/betarnd.html
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<div class="collapse navbar-collapse" id="navbarNav">
<ul class="navbar-nav">
<li class="nav-item">
<a class="nav-link" href="index.html#Distributions">
<a class="nav-link" href="index.html#Distribution Functions">
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67 changes: 18 additions & 49 deletions docs/betastat.html
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<p> Compute statistics of the Beta distribution.
</p>
<div class="ms-5">
<a name="Arguments"></a>
<h3 class="subheading">Arguments</h3>

<ul>
<li>
<var>a</var> is the first parameter of the beta distribution. <var>a</var> must be
positive

</li><li>
<var>b</var> is the second parameter of the beta distribution. <var>b</var> must be
positive
</li></ul>
<p> <var>a</var> and <var>b</var> must be of common size or one of them must be scalar
<p> <code>[<var>m</var>, <var>v</var>] = betastat (<var>a</var>, <var>b</var>)</code> returns the mean
and variance of the Beta distribution with shape parameters <var>a</var> and
<var>b</var>.
</p>
<p> The size of <var>m</var> (mean) and <var>v</var> (variance) is the common size of the
input arguments. A scalar input functions as a constant matrix of the
same size as the other inputs.
</p>
<p> Further information about the Beta distribution can be found at
<a href="https://en.wikipedia.org/wiki/Beta_distribution">https://en.wikipedia.org/wiki/Beta_distribution</a>
</p>
<p> <strong>See also: </strong>
<a href="betacdf.html">betacdf</a>,
<a href="betainv.html">betainv</a>,
<a href="betapdf.html">betapdf</a>,
<a href="betarnd.html">betarnd</a>,
<a href="betafit.html">betafit</a>,
<a href="betalike.html">betalike</a>
</p>
<a name="Return-values"></a>
<h3 class="subheading">Return values</h3>

<ul>
<li>
<var>m</var> is the mean of the beta distribution

</li><li>
<var>v</var> is the variance of the beta distribution
</li></ul>

<a name="Examples"></a>
<h3 class="subheading">Examples</h3>

<table><tr><td>&nbsp;</td><td><pre class="example"> </pre><pre class="example"> a = 1:6;
b = 1:0.2:2;
[m, v] = betastat (a, b)
</pre><pre class="example">
</pre><pre class="example"> [m, v] = betastat (a, 1.5)
</pre><pre class="example"> </pre></td></tr></table>

<a name="References"></a>
<h3 class="subheading">References</h3>

<ol>
<li>
Wendy L. Martinez and Angel R. Martinez. <cite>Computational Statistics
Handbook with MATLAB</cite>. Appendix E, pages 547-557, Chapman &amp; Hall/CRC,
2001.

</li><li>
Athanasios Papoulis. <cite>Probability, Random Variables, and Stochastic
Processes</cite>. McGraw-Hill, New York, second edition, 1984.
</li></ol>

<p><strong>Source Code: </strong>
<a href="https://github.com/gnu-octave/statistics/tree/main/inst/dist_stat/betastat.m">betastat</a>
</div>


</div>
</div>
</div>
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4 changes: 2 additions & 2 deletions docs/binocdf.html
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<div class="collapse navbar-collapse" id="navbarNav">
<ul class="navbar-nav">
<li class="nav-item">
<a class="nav-link" href="index.html#Distributions">
<a class="nav-link" href="index.html#Distribution Functions">
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Distributions
Distribution Functions
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4 changes: 2 additions & 2 deletions docs/binoinv.html
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<ul class="navbar-nav">
<li class="nav-item">
<a class="nav-link" href="index.html#Distributions">
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4 changes: 2 additions & 2 deletions docs/binopdf.html
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<div class="collapse navbar-collapse" id="navbarNav">
<ul class="navbar-nav">
<li class="nav-item">
<a class="nav-link" href="index.html#Distributions">
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4 changes: 2 additions & 2 deletions docs/binornd.html
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71 changes: 21 additions & 50 deletions docs/binostat.html
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</div>
<div class="card-body">
<dl>
<dt><u>statistics:</u> [<var>m</var>, <var>v</var>] = <b>binostat</b><i> (<var>n</var>, <var>p</var>)</i></dt>
<dt><u>statistics:</u> [<var>m</var>, <var>v</var>] = <b>binostat</b><i> (<var>n</var>, <var>ps</var>)</i></dt>
</dl>

<p> Compute statistics of the binomial distribution.
</p>
<div class="ms-5">
<a name="Arguments"></a>
<h3 class="subheading">Arguments</h3>

<ul>
<li>
<var>n</var> is the first parameter of the binomial distribution. The elements
of <var>n</var> must be natural numbers

</li><li>
<var>p</var> is the second parameter of the binomial distribution. The
elements of <var>p</var> must be probabilities
</li></ul>
<p> <var>n</var> and <var>p</var> must be of common size or one of them must be scalar
<p> <code>[<var>m</var>, <var>v</var>] = binostat (<var>n</var>, <var>ps</var>)</code> returns the mean and
variance of the binomial distribution with parameters <var>n</var> and <var>ps</var>,
where <var>n</var> is the number of trials and <var>ps</var> is the probability of
success.
</p>
<p> The size of <var>m</var> (mean) and <var>v</var> (variance) is the common size of the
input arguments. A scalar input functions as a constant matrix of the
same size as the other inputs.
</p>
<p> Further information about the binomial distribution can be found at
<a href="https://en.wikipedia.org/wiki/Binomial_distribution">https://en.wikipedia.org/wiki/Binomial_distribution</a>
</p>
<p> <strong>See also: </strong>
<a href="binocdf.html">binocdf</a>,
<a href="binoinv.html">binoinv</a>,
<a href="binopdf.html">binopdf</a>,
<a href="binornd.html">binornd</a>,
<a href="binofit.html">binofit</a>,
<a href="binolike.html">binolike</a>,
<a href="binotest.html">binotest</a>
</p>
<a name="Return-values"></a>
<h3 class="subheading">Return values</h3>

<ul>
<li>
<var>m</var> is the mean of the binomial distribution

</li><li>
<var>v</var> is the variance of the binomial distribution
</li></ul>

<a name="Examples"></a>
<h3 class="subheading">Examples</h3>

<table><tr><td>&nbsp;</td><td><pre class="example"> </pre><pre class="example"> n = 1:6;
p = 0:0.2:1;
[m, v] = binostat (n, p)
</pre><pre class="example">
</pre><pre class="example"> [m, v] = binostat (n, 0.5)
</pre><pre class="example"> </pre></td></tr></table>

<a name="References"></a>
<h3 class="subheading">References</h3>

<ol>
<li>
Wendy L. Martinez and Angel R. Martinez. <cite>Computational Statistics
Handbook with MATLAB</cite>. Appendix E, pages 547-557, Chapman &amp; Hall/CRC,
2001.

</li><li>
Athanasios Papoulis. <cite>Probability, Random Variables, and Stochastic
Processes</cite>. McGraw-Hill, New York, second edition, 1984.
</li></ol>

<p><strong>Source Code: </strong>
<a href="https://github.com/gnu-octave/statistics/tree/main/inst/dist_stat/binostat.m">binostat</a>
</div>


</div>
</div>
</div>
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4 changes: 2 additions & 2 deletions docs/bisainv.html
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4 changes: 2 additions & 2 deletions docs/burrinv.html
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