Continuous Attributes and Distributions

• Normal Distribution (Gaussian)
• Normal + Fisher
• Student's t-Distribution
• von Mises - angular (circular, cyclic, periodic) attributes.
• 1-D linear regression, y = a*x+b+N(0,σ), and polynomial-fitting.

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.

E.g., the probability density function 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