Continuous Attributes and Distributions

 home1 home2  Bib  Algorithms  Bioinfo  FP  Logic  MML  Prog.Lang and the  Book

MML
Glossary
Discrete
Continuous
Structured
SMML
KL-dist
"Art"
Ind. Inf.
Continuous

<Discrete<
>Structured>

also see
More Cts

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=
 Coding Ockham's Razor, L. Allison, Springer A Practical Introduction to Denotational Semantics, L. Allison, CUP

 Linux  Ubuntu free op. sys. OpenOffice free office suite The GIMP ~ free photoshop Firefox web browser

 Also see:  II   ACSC06   JFP05   ACSC03

 © 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.) Created with "vi (Linux + Solaris)",  charset=iso-8859-1,  fetched Wednesday, 26-Jun-2024 07:32:08 AEST.