Template:NumBlk
This template creates a numbered block which is usually used to number mathematical formulae. This template can be used together with {{EquationRef}} to produce nicely formatted numbered equations if a back reference to an equation is wanted.
Parameters
Parameters {{{1}}}, {{{2}}}, and {{{3}}} of this template are required. In addition, there are two optional parameters {{{RawN}}} and {{{LnSty}}}.
{{{1}}}: Specify indentation. The more colons (:) you put, the further indented the block will be, up to a limit of 20. This parameter can be empty if no indentation is needed.
{{{2}}}: The body or content of the block.
{{{3}}}: Specify the block number.
{{{RawN}}}: Assigned with a non-empty or non-whitespaced string to remove the formatting on the number and the parentheses surrounding the number.
{{{LnSty}}}: Specify the line style.
{{{Border}}}: If set, put a box around the equation. (Experimental.)
Examples
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Positioning relative to surrounding images
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. {{NumBlk|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.
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[math]\displaystyle{ P(a, b, \lambda) = P(a| \lambda) P(b | \lambda) P(\lambda)\, }[/math],
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}} {{NumBlk|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.
-
[math]\displaystyle{ P(a, b, \lambda) = P(a| \lambda) P(b | \lambda) P(\lambda)\, }[/math],
Eq.(6)
Table caveat
Because {{NumBlk}} is implemented as a table, putting {{NumBlk}} within a table yields a nested table. Due to a bug in Mediawiki's handling of nested tables, {{NumBlk}} 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 (:).
For example,
<dl><dd> {| |<math>(f * g)[n]\,</math> |{{NumBlk||<math>\stackrel{\mathrm{def}}{=}\sum_{m=-\infty}^{\infty} f[m]\cdot g[n - m]\,</math>| 3=<font color=darkred>'''(Eq.1)'''</font>|RawN=.}} |- | |<math>= \sum_{m=-\infty}^{\infty} f[n-m]\cdot g[m].\,</math> ([[Convolution#Commutativity|commutativity]]) |} </dd></dl>
produces
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[math]\displaystyle{ (f * g)[n]\, }[/math] [math]\displaystyle{ \stackrel{\mathrm{def}}{=}\sum_{m=-\infty}^{\infty} f[m]\cdot g[n - m]\, }[/math]
(Eq.1)
[math]\displaystyle{ = \sum_{m=-\infty}^{\infty} f[n-m]\cdot g[m].\, }[/math] (commutativity)
which shows how the outer <dl><dd> and </dd></dl> tags give the same indentation as a single colon (:) preceding the table should.
For another example,
<dl><dd> <dl><dd> {| |- |The first parameter for indentation still works when used inside table. {{NumBlk|::::|<math>ax^2+bx+c=0</math>|Level 4}} {{NumBlk|:::|<math>ax^2+bx+c=0</math>|Level 3}} {{NumBlk|::|<math>ax^2+bx+c=0</math>|Level 2}} {{NumBlk|:|<math>ax^2+bx+c=0</math>|Level 1}} {{NumBlk||<math>ax^2+bx+c=0</math>|Level 0}} |- |} </dd></dl> </dd></dl>
produces
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The first parameter for indentation still works when used inside table. -
[math]\displaystyle{ ax^2+bx+c=0 }[/math]
(Level 4)
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[math]\displaystyle{ ax^2+bx+c=0 }[/math]
(Level 3)
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[math]\displaystyle{ ax^2+bx+c=0 }[/math]
(Level 2)
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[math]\displaystyle{ ax^2+bx+c=0 }[/math]
(Level 1)
[math]\displaystyle{ ax^2+bx+c=0 }[/math]
(Level 0)
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which uses two sets of explicit tags to give the same indentation as two colons (::).