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{{technical|date=January 2014}}
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[[Image:Conditional independence.svg|thumb|right|450px|These are two examples illustrating '''conditional independence'''. Each cell represents a possible outcome. The events ''R'', ''B'' and ''Y'' are represented by the areas shaded red, blue and yellow respectively. And the probabilities of these events are shaded areas with respect to the total area. In both examples ''R'' and ''B'' are conditionally independent given ''Y'' because:
 
<math>\Pr(R \cap B \mid Y) =  \Pr(R \mid Y)\Pr(B \mid Y)\,</math><ref>To see that this is the case, one needs to realise that Pr(''R'' ∩ ''B'' | ''Y'') is the probability of an overlap of ''R'' and ''B'' (the purple shaded area) in the ''Y'' area. Since, in the picture on the left, there are two squares where ''R'' and ''B'' overlap within the ''Y'' area, and the ''Y'' area has twelve squares, Pr(''R'' ∩ ''B'' | ''Y'') = {{sfrac|2|12}} = {{sfrac|1|6}}. Similarly, Pr(''R'' | ''Y'') = {{sfrac|4|12}} = {{sfrac|1|3}} and Pr(''B'' | ''Y'') = {{sfrac|6|12}} = {{sfrac|1|2}}.</ref>  
 
<br /> but not conditionally independent given not ''Y'' because:
 
<math>\Pr(R \cap B \mid \text{not } Y) \not= \Pr(R \mid \mbox{not } Y)\Pr(B \mid \text{not } Y).\,</math>]]
 
{{Probability fundamentals}}
In [[probability theory]], two events ''R'' and ''B'' are '''conditionally independent''' given a third event ''Y'' precisely if the occurrence or non-occurrence of ''R'' ''and'' the occurrence or non-occurrence of ''B'' are [[statistical independence|independent]] events in their conditional [[probability distribution]] given ''Y''. In other words, ''R'' and ''B'' are conditionally independent given ''Y'' if and only if, given knowledge that ''Y'' occurs, knowledge of whether ''R'' occurs provides no information on the likelihood of ''B'' occurring, and knowledge of whether ''B'' occurs provides no information on the likelihood of ''R'' occurring.  
 
In the standard notation of probability theory, ''R'' and ''B'' are conditionally independent given ''Y'' if and only if
 
:<math>\Pr(R \cap B \mid Y) = \Pr(R \mid Y)\Pr(B \mid Y),\,</math>
 
or equivalently,
 
:<math>\Pr(R \mid B \cap Y) = \Pr(R \mid Y).\,</math>
 
Two [[random variable]]s ''X'' and ''Y'' are '''conditionally independent''' given a third random variable ''Z'' if and only if they are independent in their conditional probability distribution given ''Z''. That is, ''X'' and ''Y'' are conditionally independent given ''Z'' if and only if, given any value of ''Z'', the probability distribution of ''X'' is the same for all values of ''Y'' and the probability distribution of ''Y'' is the same for all values of ''X''.
 
Two events ''R'' and ''B'' are '''conditionally independent''' given a [[sigma-algebra|&sigma;-algebra]] Σ if
 
:<math>\Pr(R \cap B \mid \Sigma) = \Pr(R \mid \Sigma)\Pr(B \mid \Sigma)\ a.s.</math>
 
where <math>\Pr(A \mid \Sigma) </math> denotes the [[conditional expectation]] of the [[indicator function]] of the event <math>A</math>, <math>\chi_A</math>, given the sigma algebra <math>\Sigma</math>. That is,
 
:<math>\Pr(A \mid \Sigma) := \operatorname{E}[\chi_A\mid\Sigma].</math>
 
Two random variables ''X'' and ''Y'' are conditionally independent given a σ-algebra ''Σ'' if the above equation holds for all ''R'' in σ(''X'') and B in σ(''Y'').
 
Two random variables ''X'' and ''Y'' are conditionally independent given a random variable ''W'' if they are independent given σ(''W''): the σ-algebra generated by ''W''. This is commonly written:
 
:<math>X \perp\!\!\!\perp Y \,|\, W </math> or
:<math>X \perp Y \,|\, W</math>
 
This is read "X is independent of Y, given W"; the conditioning applies to the whole statement: "(X is independent of Y) given W".
 
:<math>(X \perp\!\!\!\perp Y) \,|\, W</math>
 
If ''W'' assumes a countable set of values, this is equivalent to the conditional independence of ''X'' and ''Y'' for the events of the form [''W''&nbsp;=&nbsp;''w''].
Conditional independence of more than two events, or of more than two random variables, is defined analogously.
 
The following two examples show that ''X'' {{Unicode|&perp;}} ''Y''
''neither implies nor is implied by'' ''X'' {{Unicode|&perp;}} ''Y | W''.
First, suppose ''W'' is 0 with probability 0.5 and is the value 1 otherwise.  When
''W''&nbsp;=&nbsp;0 take ''X'' and ''Y'' to be independent, each having the value 0 with probability 0.99 and the value 1 otherwise.  When ''W''&nbsp;=&nbsp;1, ''X'' and ''Y'' are again independent, but this time they take the value 1
with probability 0.99.  Then ''X'' {{Unicode|&perp;}} ''Y''&nbsp;|&nbsp;''W''.  But ''X'' and ''Y'' are dependent, because Pr(''X''&nbsp;=&nbsp;0) < Pr(''X''&nbsp;=&nbsp;0|''Y''&nbsp;=&nbsp;0).  This is because Pr(''X''&nbsp;=&nbsp;0) =&nbsp;0.5, but if ''Y''&nbsp;=&nbsp;0 then it's very likely that ''W''&nbsp;=&nbsp;0 and thus that ''X''&nbsp;=&nbsp;0 as well, so Pr(''X''&nbsp;=&nbsp;0|''Y''&nbsp;=&nbsp;0)&nbsp;>&nbsp;0.5.  For the second example, suppose ''X'' {{Unicode|&perp;}} ''Y'', each taking the values 0 and 1 with probability&nbsp;0.5.  Let ''W'' be the product ''X''{{Unicode|&times;}}''Y''.  Then when ''W''&nbsp;=&nbsp;0, Pr(''X''&nbsp;=&nbsp;0)&nbsp;=&nbsp;2/3, but Pr(''X''&nbsp;=&nbsp;0|''Y''&nbsp;=&nbsp;0)&nbsp;=&nbsp;1/2, so ''X''&nbsp;{{Unicode|&perp;}}&nbsp;''Y''&nbsp;|&nbsp;''W'' is false.
This is also an example of Explaining Away.  See Kevin Murphy's tutorial
<ref>http://people.cs.ubc.ca/~murphyk/Bayes/bnintro.html</ref>
where ''X'' and ''Y'' take the values "brainy" and "sporty".
 
==Uses in Bayesian inference==
Let ''p'' be the proportion of voters who will vote "yes" in an upcoming [[referendum]]. In taking an [[opinion poll]], one chooses ''n'' voters randomly from the population. For ''i''&nbsp;=&nbsp;1,&nbsp;...,&nbsp;''n'', let ''X''<sub>''i''</sub> =&nbsp;1 or 0 according as the ''i''th chosen voter will or will not vote "yes".
 
In a [[frequency probability|frequentist]] approach to [[statistical inference]] one would not attribute any probability distribution to ''p'' (unless the probabilities could be somehow interpreted as relative frequencies of occurrence of some event or as proportions of some population) and one would say that ''X''<sub>1</sub>, ..., ''X''<sub>''n''</sub> are [[statistical independence|independent]] random variables.
 
By contrast, in a [[Bayesian inference|Bayesian]] approach to statistical inference, one would assign a [[probability distribution]] to ''p'' regardless of the non-existence of any such "frequency" interpretation, and one would construe the probabilities as degrees of belief that ''p'' is in any interval to which a probability is assigned. In that model, the random variables ''X''<sub>1</sub>,&nbsp;...,&nbsp;''X''<sub>''n''</sub> are ''not'' independent, but they are '''conditionally independent''' given the value of ''p''. In particular, if a large number of the ''X''s are observed to be equal to 1, that would imply a high conditional probability, given that observation, that ''p'' is near 1, and thus a high conditional probability, given that observation, that the ''next'' ''X'' to be observed will be equal to 1.
 
==Rules of conditional independence==
 
A set of rules governing statements of conditional independence have been derived from the basic definition.<ref>{{cite journal
|first=A. P. |last=Dawid |authorlink=Philip Dawid
|title=Conditional Independence in Statistical Theory
|journal=[[Journal of the Royal Statistical Society, Series B]]
|year=1979
|volume=41 |issue=1 |pages=1–31
|mr=0535541
|jstor=2984718
}}</ref><ref>J Pearl, Causality: Models, Reasoning, and Inference, 2000, Cambridge University Press</ref>
 
Note: since these implications hold for any probability space, they will still hold if one considers a sub-universe by conditioning everything on another variable, say&nbsp;''K''. For example, <math>X \perp\!\!\!\perp Y \Rightarrow Y \perp\!\!\!\perp X</math> would also mean that <math>X \perp\!\!\!\perp Y \mid K  \Rightarrow Y \perp\!\!\!\perp X \mid K</math>.
 
Note: below, the comma can be read as an "AND".
 
===Symmetry===
: <math>
X \perp\!\!\!\perp Y
\quad \Rightarrow \quad
Y \perp\!\!\!\perp X
</math>
 
===Decomposition===
: <math>
X \perp\!\!\!\perp A,B
\quad \Rightarrow \quad
\text{ and }
\begin{cases}
  X \perp\!\!\!\perp A \\
  X \perp\!\!\!\perp B
\end{cases}
</math>
 
Proof:
* <math>
p_{X,A,B}(x,a,b) = p_X(x) p_{A,B}(a,b)
</math> &nbsp;&nbsp;&nbsp;&nbsp; (meaning of <math>X \perp A,B</math>)
* <math>
\int_{B} \! p_{X,A,B}(x,a,b) = \int_{B} \! p_X(x) p_{A,B}(a,b)
</math> &nbsp;&nbsp;&nbsp;&nbsp; (ignore variable ''B'' by integrating it out)
* <math>
p_{X,A}(x,a) = p_X(x) p_A(a)
</math> &nbsp;&nbsp;&nbsp;&nbsp;
 
A similar proof shows the independence of ''X'' and ''B''.
 
===Weak union===
 
: <math>
X \perp\!\!\!\perp A,B
\quad \Rightarrow \quad
X \perp\!\!\!\perp A \mid B
</math>
 
===Contraction===
 
: <math>
\left.\begin{align}
  X \perp\!\!\!\perp A \mid B \\
  X \perp\!\!\!\perp B
\end{align}\right\}\text{ and }
\quad \Rightarrow \quad
X \perp\!\!\!\perp A,B
</math>
 
===Contraction-weak-union-decomposition===
 
Putting the above three together, we have:
 
: <math>
\left.\begin{align}
  X \perp\!\!\!\perp A \mid B \\
  X \perp\!\!\!\perp B
\end{align}\right\}\text{ and }
\quad \iff \quad
X \perp\!\!\!\perp A,B
\quad \Rightarrow \quad
\text{ and }
\begin{cases}
  X \perp\!\!\!\perp A \mid B \\
  X \perp\!\!\!\perp B \\
  X \perp\!\!\!\perp B \mid A \\
  X \perp\!\!\!\perp A \\
\end{cases}
</math>
 
===Intersection===
 
If the probabilities of ''X'', ''A'', ''B'' are all positive{{Citation needed|date=October 2010}}, then the following also holds:
 
: <math>
\left.\begin{align}
  X \perp\!\!\!\perp A \mid B \\
  X \perp\!\!\!\perp B \mid A
\end{align}\right\}\text{ and }
\quad \Rightarrow \quad
X \perp\!\!\!\perp A, B
</math>
 
==See also==
*[[Conditional dependence]]
*[[Cointelation]]
*[[de Finetti's theorem]]
*[[Conditional expectation]]
 
==References==
{{Reflist}}
 
{{DEFAULTSORT:Conditional Independence}}
[[Category:Probability theory]]
[[Category:Statistical dependence]]

Latest revision as of 12:03, 2 January 2015

Making your computer run quickly is very easy. Most computers run slow because they are jammed up with junk files, which Windows has to search through every time it wants to obtain something. Imagine having to find a book inside a library, nevertheless all the library books are inside a big huge pile. That's what it's like for your computer to obtain something, when your system is full of junk files.

You might discover that there are registry products which are free plus those that you'll have to pay a nominal sum for. Some registry cleaners provide a bare bones program for free with the option of upgrading to a more advanced, efficient adaptation of the same program.

So, this advanced double scan is not merely one of the better, but it is very equally freeware. And as of all of this that countless regard CCleaner among the better registry cleaners in the marketplace now. I would add which I personally prefer Regcure for the simple reason which it has a greater interface plus I recognize for a truth which it is ad-ware without charge.

The issue with nearly all of the folks is that they do not like to invest cash. In the damaged adaptation 1 does not have to pay anything plus can download it from internet especially easily. It is simple to install also. But, the issue comes whenever it is actually unable to identify all possible viruses, spyware and malware inside the program. This really is considering it's obsolete in nature and does not get any regular updates within the url downloaded. Thus, your system is accessible to difficulties like hacking.

One other way whenever arresting the 1328 error is to wash out a PC's registry. The registry is surprisingly important because it's where settings plus files employed by Windows for running are stored. As it really is frequently chosen, breakdowns and instances of files getting corrupted are not uncommon. Additionally considering of the means it is configured, the "registry" gets saved in the wrong fashion regularly, that makes the system run slow, eventually causing the PC to suffer from a series of errors. The many effective way 1 will use in cleaning out registries is to use a reliable tuneup utilities program. A registry cleaner can seek out plus repair corrupted registry files plus settings allowing one's computer to run usually again.

The initial thing we should do is to reinstall any program that shows the error. It's typical for countless computers to have certain programs which need this DLL to show the error whenever you try and load it up. If you see a certain program show the error, you need to first uninstall which program, restart your PC and then resinstall the system again. This could substitute the damaged ac1st16.dll file and remedy the error.

Your registry is the region all the important configurations for hardware, software plus user profile configurations plus preferences are stored. Every time 1 of these things is changed, the database then begins to expand. Over time, the registry will become bloated with unnecessary files. This causes a general slow down yet in extreme cases can result important jobs and programs to stop functioning all together.

So in summary, when comparing registry cleaning, make certain that the one we choose provides we the following.A backup and restore center, rapidly operation, automatic deletion facility, start-up management, an effortless way of contact along with a cash back guarantee.