Continuous Attributes and Distributions

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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
N , ( )

hi= N(x) dx
lo=
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© L. Allison   http://www.allisons.org/ll/   (or as otherwise indicated),
Faculty of Information Technology (Clayton), Monash University, Australia 3800 (6/'05 was School of Computer Science and Software Engineering, Fac. Info. Tech., Monash University,
was Department of Computer Science, Fac. Comp. & Info. Tech., '89 was Department of Computer Science, Fac. Sci., '68-'71 was Department of Information Science, Fac. Sci.)
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