## Continuous Attributes and Distributions

- Normal Distribution (Gaussian).
- Normal + Fisher.
- Laplace
- Student's t-Distribution.
- von Mises - Fisher,
directions in
**R**^{D}, and von Mises, angular (circular, cyclic, periodic) attributes, where D = 2. - 1-D linear regression, y = a*x+b+N(0,σ), and polynomial-fitting.
- Discretization

This section and its sub-pages are about continuous probability distributions such as the normal distribution (Gaussian distribution), and estimating the parameters of such distributions from given data.

- For example, the probability density function (pdf) of
a
*normal distribution*, N(μ, σ), with*mean*μ and*standard deviation*σ > 0, for -∞ < x < ∞, is: - f(x) =
(1 / √(2π).σ) .
e
^{-(x-μ)2 / 2σ2} - Note that f(x) is symmetric about x=μ, and it is the case, of course, that
_{-∞}∫^{+∞}f(x) dx = 1