## Continuous Attributes and Distributions

 LA home Computing MML  Glossary  Continuous  Structured> also see Continuous

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
N , ( )

 ∫ hi= N(x) dx lo=

ε= (i.e., [x-ε/2,x+ε/2]), ε-discretised-entropy nits = bits
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