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jointly continuous randoms variables - tradução para russo
MATHEMATICAL PRACTICE
Continuous model; Continuous Model; Continuous Modelling
jointly continuous randoms variables
совместно непрерывные случайные величины
continuous variable
WIKIPEDIA ARTICLE COVERING MULTIPLE TOPICS
Discrete number; Discrete variable; Continuous variable; Continuous variables; Discrete variables; Continuous data; Discrete and continuous variables; Quantitative variable; Continuous and discrete variables; Continuous and discrete variable; Discrete value
математика
непрерывная переменная
continuous variable
WIKIPEDIA ARTICLE COVERING MULTIPLE TOPICS
Discrete number; Discrete variable; Continuous variable; Continuous variables; Discrete variables; Continuous data; Discrete and continuous variables; Quantitative variable; Continuous and discrete variables; Continuous and discrete variable; Discrete value
непрерывная переменная
Definição
continuous function
A function f : D -> E, where D and E are cpos, is continuous
if it is monotonic and
f (lub Z) = lub f z | z in Z
for all directed sets Z in D. In other words, the image of
the lub is the lub of any directed image.
All additive functions (functions which preserve all lubs)
are continuous. A continuous function has a {least fixed
point} if its domain has a least element, bottom (i.e. it
is a cpo or a "pointed cpo" depending on your definition of a
cpo). The least fixed point is
fix f = lub f^n bottom | n = 0..infinity
(1994-11-30)
Wikipédia
Continuous modelling
Continuous modelling is the mathematical practice of applying a model to continuous data (data which has a potentially infinite number, and divisibility, of attributes). They often use differential equations and are converse to discrete modelling.
Modelling is generally broken down into several steps:
- Making assumptions about the data: The modeller decides what is influencing the data and what can be safely ignored.
- Making equations to fit the assumptions.
- Solving the equations.
- Verifying the results: Various statistical tests are applied to the data and the model and compared.
- If the model passes the verification progress, putting it into practice.
- If the model fails the verification progress, altering it and subjecting it again to verification; if it persists in fitting the data more poorly than a competing model, it is abandoned.
