
This section and its subpages 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^{(xμ)2 / 2σ2}

 Note that f(x) is symmetric about x=μ, and
it is the case, of course, that

 _{∞}∫^{+∞} f(x) dx = 1

window on the wide world:

