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26<div class="titlepage"><div><div><h3 class="title">
27<a name="math_toolkit.stat_tut.dist_params"></a><a class="link" href="dist_params.html" title="Discrete Probability Distributions">Discrete Probability
28 Distributions</a>
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30<p>
31 Note that the <a href="http://en.wikipedia.org/wiki/Discrete_probability_distribution" target="_top">discrete
32 distributions</a>, including the binomial, negative binomial, Poisson
33 &amp; Bernoulli, are all mathematically defined as discrete functions: only
34 integral values of the <a href="http://en.wikipedia.org/wiki/Random_variate" target="_top">random
35 variate</a> are envisaged and the functions are only defined at these
36 integral values. However because the method of calculation often uses continuous
37 functions, it is convenient to treat them as if they were continuous functions,
38 and permit non-integral values of their parameters.
39 </p>
40<p>
41 To enforce a strict mathematical model, users may use floor or ceil functions
42 on the <a href="http://en.wikipedia.org/wiki/Random_variate" target="_top">random variate</a>,
43 prior to calling the distribution function, to enforce integral values.
44 </p>
45<p>
46 For similar reasons, in continuous distributions, parameters like degrees
47 of freedom that might appear to be integral, are treated as real values (and
48 are promoted from integer to floating-point if necessary). In this case however,
49 that there are a small number of situations where non-integral degrees of
50 freedom do have a genuine meaning.
51 </p>
52<p>
53 Generally speaking there is no loss of performance from allowing real-values
54 parameters: the underlying special functions contain optimizations for integer-valued
55 arguments when applicable.
56 </p>
57<div class="caution"><table border="0" summary="Caution">
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60<th align="left">Caution</th>
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63<p>
64 The quantile function of a discrete distribution will by default return
65 an integer result that has been <span class="emphasis"><em>rounded outwards</em></span>.
66 That is to say lower quantiles (where the probability is less than 0.5)
67 are rounded downward, and upper quantiles (where the probability is greater
68 than 0.5) are rounded upwards. This behaviour ensures that if an X% quantile
69 is requested, then <span class="emphasis"><em>at least</em></span> the requested coverage
70 will be present in the central region, and <span class="emphasis"><em>no more than</em></span>
71 the requested coverage will be present in the tails.
72 </p>
73<p>
74 This behaviour can be changed so that the quantile functions are rounded
75 differently, or even return a real-valued result using <a class="link" href="../pol_overview.html" title="Policy Overview">Policies</a>.
76 It is strongly recommended that you read the tutorial <a class="link" href="../pol_tutorial/understand_dis_quant.html" title="Understanding Quantiles of Discrete Distributions">Understanding
77 Quantiles of Discrete Distributions</a> before using the quantile function
78 on a discrete distribution. The <a class="link" href="../pol_ref/discrete_quant_ref.html" title="Discrete Quantile Policies">reference
79 docs</a> describe how to change the rounding policy for these distributions.
80 </p>
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