Changeset 1342 for trunk/yat


Ignore:
Timestamp:
Jun 18, 2008, 6:09:29 PM (13 years ago)
Author:
Peter
Message:

fixing doxygen problems

File:
1 edited

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  • trunk/yat/random/random.h

    r1275 r1342  
    275275  };
    276276
    277   ///
    278   /// @brief Discrete uniform distribution
    279   ///
    280   /// Discrete uniform distribution also known as the "equally likely
    281   /// outcomes" distribution. Each outcome, in this case an integer
    282   /// from [0,n-1] , have equal probability to occur.
    283   ///
    284   /// Distribution function \f$ p(k) = \frac{1}{n+1} \f$ for \f$ 0 \le
    285   /// k < n \f$ \n
    286   /// Expectation value: \f$ \frac{n-1}{2} \f$ \n
    287   /// Variance: \f$ \frac{1}{12}(n-1)(n+1) \f$
    288   ///
     277  /**
     278    @brief Discrete uniform distribution
     279 
     280    Discrete uniform distribution also known as the "equally likely
     281    outcomes" distribution. Each outcome, in this case an integer
     282    from [0,n-1] , have equal probability to occur.
     283     
     284    Distribution function \f$ p(k) = \frac{1}{n+1} \f$ for \f$ 0 \le
     285    k < n \f$ \n
     286    Expectation value: \f$ \frac{n-1}{2} \f$ \n
     287    Variance: \f$ \frac{1}{12}(n-1)(n+1) \f$
     288  */
    289289  class DiscreteUniform : public Discrete
    290290  {
     
    332332  };
    333333
    334   ///
    335   /// @brief Poisson Distribution
    336   ///
    337   /// Having a Poisson process (i.e. no memory), number of occurences
    338   /// within a given time window is Poisson distributed. This
    339   /// distribution is the limit of a Binomial distribution when number
    340   /// of attempts is large, and the probability for one attempt to be
    341   /// succesful is small (in such a way that the expected number of
    342   /// succesful attempts is \f$ m \f$.
    343   ///
    344   /// Probability function \f$ p(k) = e^{-m}\frac{m^k}{k!} \f$ for \f$ 0 \le
    345   /// k  \f$ \n
    346   /// Expectation value: \f$ m \f$ \n
    347   /// Variance: \f$ m \f$
    348   ///
     334  /**
     335    @brief Poisson Distribution
     336 
     337    Having a Poisson process (i.e. no memory), number of occurences
     338    within a given time window is Poisson distributed. This
     339    distribution is the limit of a Binomial distribution when number
     340    of attempts is large, and the probability for one attempt to be
     341    succesful is small (in such a way that the expected number of
     342    succesful attempts is \f$ m \f$.
     343     
     344    Probability function \f$ p(k) = e^{-m}\frac{m^k}{k!} \f$ for \f$ 0 \le
     345    k  \f$ \n
     346    Expectation value: \f$ m \f$ \n
     347    Variance: \f$ m \f$
     348  */
    349349  class Poisson : public Discrete
    350350  {
     
    413413    /// @see seed, RNG::seed_from_devurandom, RNG::seed
    414414    ///
    415     unsigned long seed_from_devurandom(void) { return rng_->seed_from_devurandom(); }
     415    unsigned long seed_from_devurandom(void)
     416    { return rng_->seed_from_devurandom(); }
    416417
    417418    ///
     
    472473  };
    473474
    474   ///
    475   /// @brief Generator of random numbers from an exponential
    476   /// distribution.
    477   ///
    478   /// The distribution function is \f$ f(x) = \frac{1}{m}\exp(-x/a)
    479   /// \f$ for \f$ x \f$ with the expectation value \f$ m \f$ and
    480   /// variance \f$ m^2 \f$
    481   ///
     475  /**
     476     \brief Generator of random numbers from an exponential
     477    distribution.
     478     
     479    The distribution function is \f$ f(x) = \frac{1}{m}\exp(-x/a)
     480    \f$ for \f$ x \f$ with the expectation value \f$ m \f$ and
     481    variance \f$ m^2 \f$
     482  */
    482483  class Exponential : public Continuous
    483484  {
     
    507508  };
    508509
    509   ///
    510   /// @brief Gaussian distribution
    511   ///
    512   /// Class for generating a random number from a Gaussian
    513   /// distribution between zero and unity. Utilizes the Box-Muller
    514   /// algorithm, which needs two calls to random generator.
    515   ///
    516   /// Distribution function \f$ f(x) =
    517   /// \frac{1}{\sqrt{2\pi\sigma^2}}\exp(-\frac{(x-\mu)^2}{2\sigma^2})
    518   /// \f$ \n
    519   /// Expectation value: \f$ \mu \f$ \n
    520   /// Variance: \f$ \sigma^2 \f$
    521   ///
     510  /**
     511    @brief Gaussian distribution
     512     
     513     Class for generating a random number from a Gaussian distribution
     514     between zero and unity. Utilizes the Box-Muller algorithm, which
     515    needs two calls to random generator.
     516     
     517    Distribution function \f$ f(x) =
     518    \frac{1}{\sqrt{2\pi\sigma^2}}\exp(-\frac{(x-\mu)^2}{2\sigma^2})
     519    \f$ \n
     520    Expectation value: \f$ \mu \f$ \n
     521    Variance: \f$ \sigma^2 \f$
     522  */
    522523  class Gaussian : public Continuous
    523524  {
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