Параметры {{{1}}}, {{{2}}}, и {{{3}}} шаблона обязательны. Дополнительно, есть две возможных опции {{{RawN}}} и {{{LnSty}}}.
{{{1}}}
: Определяет отступ. Чем больше здесь двоеточий (:), тем дальше вправо сдвинется блок. Поддерживается не более 20 двоеточий. Этот параметр может быть пустой.
{{{2}}}
: Сама формула, оформленная тэгами <math> или <chem>.
{{{3}}}
: Номер формулы.
{{{RawN}}}
: Если непустой, то убирает скобки и форматирование номера формулы.
{{{LnSty}}}
: Стиль линии.
Numbered blocks should be able to be placed around images that take up space on the left or right side of the screen. To ensure numbered block has access to the entire line, consider using a
{{
clear
}}
-like template.
To illustrate, consider the example:
<!-- [[Image:Bnet fan2.png|frame|right|Fig.1: Bayesian Network representation of Eq.(6)]]
[[Image:Bnet fan2.png|frame|left|Fig.1: Bayesian Network representation of Eq.(6)]]-->
<br><br>A Bayesian network (or a belief network) is a probabilistic graphical model that represents a set of
variables and their probabilistic independencies. For example, a Bayesian network could represent the
probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute
the probabilities of the presence of various diseases.
{{Нумерованная формула|1=:|2=<math>
P(a, b, \lambda) = P(a| \lambda) P(b | \lambda) P(\lambda)\,
</math>,|3='''Eq.(6)'''|RawN=.}}
A Bayesian network (or a belief network) is a probabilistic graphical model that represents a set of
variables and their probabilistic independencies. For example, a Bayesian network could represent the
probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute
the probabilities of the presence of various diseases.
,
Eq.(6)
If it is desirable for the numbered block to span the entire line, a
{{
clear
}}
should be placed before it.
<!-- [[Image:Bnet fan2.png|frame|right|Fig.1: Bayesian Network representation of Eq.(6)]]
[[Image:Bnet fan2.png|frame|left|Fig.1: Bayesian Network representation of Eq.(6)]]-->
<br><br>A Bayesian network (or a belief network) is a probabilistic graphical model that represents a set of
variables and their probabilistic independencies. For example, a Bayesian network could represent the
probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute
the probabilities of the presence of various diseases.
{{clear}}
{{Нумерованная формула|1=:|2=<math>
P(a, b, \lambda) = P(a| \lambda) P(b | \lambda) P(\lambda)\,
</math>,|3='''Eq.(6)'''|RawN=.}}
A Bayesian network (or a belief network) is a probabilistic graphical model that represents a set of
variables and their probabilistic independencies. For example, a Bayesian network could represent the
probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute
the probabilities of the presence of various diseases.
,
Eq.(6)
Table caveat
Because
{{
Нумерованная формула
}}
is implemented as a table, putting
{{
Нумерованная формула
}}
within a table yields a
. Due to a bug in
Mediawiki
's handling of nested tables,
{{
Нумерованная формула
}}
must be used carefully in this case. In particular, when indentation for the outer table is desired, use explicit
<dl><dd>
and
</dd></dl>
tags for indentation instead of a leading colon (:).