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85 lines
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85 lines
3.7 KiB
Plaintext
[section:variates Random Variates and Distribution Parameters]
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[@http://en.wikipedia.org/wiki/Random_variate Random variates]
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and [@http://en.wikipedia.org/wiki/Parameter distribution parameters]
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are conventionally distinguished (for example in Wikipedia and Wolfram MathWorld
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by placing a semi-colon after the __random_variate (whose value you 'choose'),
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to separate the variate from the parameter(s) that defines the shape of the distribution.
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For example, the binomial distribution has two parameters:
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n (the number of trials) and p (the probability of success on one trial).
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It also has the __random_variate /k/: the number of successes observed.
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This means the probability density\/mass function (pdf) is written as ['f(k; n, p)].
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Translating this into code the `binomial_distribution` constructor
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therefore has two parameters:
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binomial_distribution(RealType n, RealType p);
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While the function `pdf` has one argument specifying the distribution type
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(which includes its parameters, if any),
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and a second argument for the __random_variate. So taking our binomial distribution
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example, we would write:
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pdf(binomial_distribution<RealType>(n, p), k);
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[endsect]
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[section:dist_params Discrete Probability Distributions]
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Note that the [@http://en.wikipedia.org/wiki/Discrete_probability_distribution
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discrete distributions], including the binomial, negative binomial, Poisson & Bernoulli,
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are all mathematically defined as discrete functions:
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only integral values of the __random_variate are envisaged
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and the functions are only defined at these integral values.
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However because the method of calculation often uses continuous functions,
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it is convenient to treat them as if they were continuous functions,
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and permit non-integral values of their parameters.
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To enforce a strict mathematical model,
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users may use floor or ceil functions on the __random_variate,
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prior to calling the distribution function, to enforce integral values.
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For similar reasons, in continuous distributions, parameters like degrees of freedom
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that might appear to be integral, are treated as real values
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(and are promoted from integer to floating-point if necessary).
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In this case however, that there are a small number of situations where non-integral
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degrees of freedom do have a genuine meaning.
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Generally speaking there is no loss of performance from allowing real-values
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parameters: the underlying special functions contain optimizations for
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integer-valued arguments when applicable.
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[caution
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The quantile function of a discrete distribution will by
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default return an integer result that has been
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/rounded outwards/. That is to say lower quantiles (where the probability is
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less than 0.5) are rounded downward, and upper quantiles (where the probability
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is greater than 0.5) are rounded upwards. This behaviour
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ensures that if an X% quantile is requested, then /at least/ the requested
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coverage will be present in the central region, and /no more than/
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the requested coverage will be present in the tails.
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This behaviour can be changed so that the quantile functions are rounded
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differently, or even return a real-valued result using
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[link math_toolkit.pol_overview Policies]. It is strongly
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recommended that you read the tutorial
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[link math_toolkit.pol_tutorial.understand_dis_quant
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Understanding Quantiles of Discrete Distributions] before
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using the quantile function on a discrete distribution. The
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[link math_toolkit.pol_ref.discrete_quant_ref reference docs]
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describe how to change the rounding policy
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for these distributions.
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]
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[endsect]
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[/
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Copyright 2006 John Maddock and Paul A. Bristow.
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Distributed under the Boost Software License, Version 1.0.
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(See accompanying file LICENSE_1_0.txt or copy at
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http://www.boost.org/LICENSE_1_0.txt).
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]
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