# Changeset 1342

Ignore:
Timestamp:
Jun 18, 2008, 6:09:29 PM (14 years ago)
Message:

fixing doxygen problems

File:
1 edited

### Legend:

Unmodified
 r1275 }; /// /// @brief Discrete uniform distribution /// /// Discrete uniform distribution also known as the "equally likely /// outcomes" distribution. Each outcome, in this case an integer /// from [0,n-1] , have equal probability to occur. /// /// Distribution function \f$p(k) = \frac{1}{n+1} \f$ for \f$0 \le /// k < n \f$ \n /// Expectation value: \f$\frac{n-1}{2} \f$ \n /// Variance: \f$\frac{1}{12}(n-1)(n+1) \f$ /// /** @brief Discrete uniform distribution Discrete uniform distribution also known as the "equally likely outcomes" distribution. Each outcome, in this case an integer from [0,n-1] , have equal probability to occur. Distribution function \f$p(k) = \frac{1}{n+1} \f$ for \f$0 \le k < n \f$ \n Expectation value: \f$\frac{n-1}{2} \f$ \n Variance: \f$\frac{1}{12}(n-1)(n+1) \f$ */ class DiscreteUniform : public Discrete { }; /// /// @brief Poisson Distribution /// /// Having a Poisson process (i.e. no memory), number of occurences /// within a given time window is Poisson distributed. This /// distribution is the limit of a Binomial distribution when number /// of attempts is large, and the probability for one attempt to be /// succesful is small (in such a way that the expected number of /// succesful attempts is \f$m \f$. /// /// Probability function \f$p(k) = e^{-m}\frac{m^k}{k!} \f$ for \f$0 \le /// k \f$ \n /// Expectation value: \f$m \f$ \n /// Variance: \f$m \f$ /// /** @brief Poisson Distribution Having a Poisson process (i.e. no memory), number of occurences within a given time window is Poisson distributed. This distribution is the limit of a Binomial distribution when number of attempts is large, and the probability for one attempt to be succesful is small (in such a way that the expected number of succesful attempts is \f$m \f$. Probability function \f$p(k) = e^{-m}\frac{m^k}{k!} \f$ for \f$0 \le k \f$ \n Expectation value: \f$m \f$ \n Variance: \f$m \f$ */ class Poisson : public Discrete { /// @see seed, RNG::seed_from_devurandom, RNG::seed /// unsigned long seed_from_devurandom(void) { return rng_->seed_from_devurandom(); } unsigned long seed_from_devurandom(void) { return rng_->seed_from_devurandom(); } /// }; /// /// @brief Generator of random numbers from an exponential /// distribution. /// /// The distribution function is \f$f(x) = \frac{1}{m}\exp(-x/a) /// \f$ for \f$x \f$ with the expectation value \f$m \f$ and /// variance \f$m^2 \f$ /// /** \brief Generator of random numbers from an exponential distribution. The distribution function is \f$f(x) = \frac{1}{m}\exp(-x/a) \f$ for \f$x \f$ with the expectation value \f$m \f$ and variance \f$m^2 \f$ */ class Exponential : public Continuous { }; /// /// @brief Gaussian distribution /// /// Class for generating a random number from a Gaussian /// distribution between zero and unity. Utilizes the Box-Muller /// algorithm, which needs two calls to random generator. /// /// Distribution function \f$f(x) = /// \frac{1}{\sqrt{2\pi\sigma^2}}\exp(-\frac{(x-\mu)^2}{2\sigma^2}) /// \f$ \n /// Expectation value: \f$\mu \f$ \n /// Variance: \f$\sigma^2 \f$ /// /** @brief Gaussian distribution Class for generating a random number from a Gaussian distribution between zero and unity. Utilizes the Box-Muller algorithm, which needs two calls to random generator. Distribution function \f$f(x) = \frac{1}{\sqrt{2\pi\sigma^2}}\exp(-\frac{(x-\mu)^2}{2\sigma^2}) \f$ \n Expectation value: \f$\mu \f$ \n Variance: \f$\sigma^2 \f$ */ class Gaussian : public Continuous {