NormalSampler.h File Reference

Represents a normal distribution: X ~ N(u, sigma^2) f(x) = 1/(sqrt(2*pi*sigma^2)*exp(-(x-u)^2/(2*sigma^2))) The normal distribution (aka Gaussian) is parameterized by its mean and variance and is referred to as the standard normal distribution when when it has mean zero and variance one. The normal distribution is probably the single most important distribution in all of probability because of its ubiquity in describing natural phenomena and because of the Central Limit Theorem, which says that the sum of a sufficiently large number of iid random variables, each with finite mean and varirance, will be approximately normally distributed. This allows one to study almost any distribution in terms of one, standard normal, distribution, simplifying many computations and proofs all over both applied and theoretical statistics. More...

#include <stats/Sampler.h>

Go to the source code of this file.

Classes
class	NormalSampler
	Represents a normal distribution: X ~ N(u, sigma^2) f(x) = 1/(sqrt(2*pi*sigma^2)*exp(-(x-u)^2/(2*sigma^2))) The normal distribution (aka Gaussian) is parameterized by its mean and variance and is referred to as the standard normal distribution when when it has mean zero and variance one. The normal distribution is probably the single most important distribution in all of probability because of its ubiquity in describing natural phenomena and because of the Central Limit Theorem, which says that the sum of a sufficiently large number of iid random variables, each with finite mean and varirance, will be approximately normally distributed. This allows one to study almost any distribution in terms of one, standard normal, distribution, simplifying many computations and proofs all over both applied and theoretical statistics. More...

---

Detailed Description

Represents a normal distribution: X ~ N(u, sigma^2) f(x) = 1/(sqrt(2*pi*sigma^2)*exp(-(x-u)^2/(2*sigma^2))) The normal distribution (aka Gaussian) is parameterized by its mean and variance and is referred to as the standard normal distribution when when it has mean zero and variance one. The normal distribution is probably the single most important distribution in all of probability because of its ubiquity in describing natural phenomena and because of the Central Limit Theorem, which says that the sum of a sufficiently large number of iid random variables, each with finite mean and varirance, will be approximately normally distributed. This allows one to study almost any distribution in terms of one, standard normal, distribution, simplifying many computations and proofs all over both applied and theoretical statistics.

Author:

Travis Fischer (fisch0920@gmail.com)

Matthew Jacobs (jacobs.mh@gmail.com)

Date:

Fall 2008

Definition in file NormalSampler.h.