Approximate entropy - Revision history

en>Jayjg: clean up, fix grammar/shorter using AWB

2014-11-03T01:25:15Z

clean up, fix grammar/shorter using AWB

← Older revision		Revision as of 03:25, 3 November 2014
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	~~I'm a 41 years old~~, ~~married~~ and work at the high school (Asian Studies).<br>In my spare time I try to teach myself [http://www.taskrb.com/%E5%93%81%E8%B3%AA%E3%81%AF%E9%9D%9E%E5%B8%B8%E3%81%AB%E8%89%AF%E3%81%84-%E3%83%9B%E3%83%83%E3%82%B1%E3%83%BC-%E6%96%B0%E7%9D%80/%E6%9C%89%E5%90%8D%E3%83%96%E3%83%A9%E3%83%B3%E3%83%88-%E3%82%B7%E3%83%A7%E3%83%AB%E3%83%80%E3%83%BC-%E5%8E%B3%E9%81%B8%E3%81%97%E5%88%B6%E4%BD%9C%E3%81%97%E3%81%9F/%E6%96%B0%E3%81%97%E3%81%8F%E7%9D%80%E3%81%8D-easton-%E3%82%A4%E3%83%BC%E3%82%B9%E3%83%88%E3%83%B3-%E6%96%B0%E4%BD%9C/%E7%89%B9%E3%81%AB%E5%A3%B2%E3%82%8C%E3%81%A6%E3%81%84%E3%82%8B-jr-%E3%82%B8%E3%83%A5%E3%83%8B%E3%82%A2-130%E3%80%9C160cm-%E8%B2%A9%E5%A3%B2 ホッケー jr ジュニア 130〜160cm] Korean. I've [http://www.~~taskrb~~.com/~~%E9%AB%98%E5%93%81%E8%B3%AA-%E6%AD%A6%E9%81%93-%E5%B0%82%E5%A3%B2%E5%BA%97~~/~~%E5%BD%93%E5%BA%97%E3%81%AE%E8%AA%A0%E5%AE%9F-%E8%A9%A6%E5%90%88%E3%83%BB%E6%BC%94%E8%88%9E%E7%94%A8%E5%93%81-%E3%82%A2%E3%82%A6%E3%83%88%E3%83%AC%E3%83%83%E3%83%88 武道試合演舞用品~~] ~~been twicethere~~ and ~~look forward to returning sometime in [http://www~~.taskrb.com/%E6%9C%89%E5%90%8D%E3%83%96%E3%83%A9%E3%83%B3%E3%83%88-%E3%83%90%E3%82%B9%E3%82%B1%E3%83%83%E3%83%88%E3%83%9C%E3%83%BC%E3%83%AB-%E6%96%B0%E5%93%81/%E5%A4%A7%E4%BA%BA%E6%B0%97-%E3%83%9C%E3%83%BC%E3%83%AB-%E5%B0%82%E5%A3%B2%E5%BA%97/%E8%B6%85%E5%AE%89%E3%81%84-%EF%BC%97%E5%8F%B7%E3%83%9C%E3%83%BC%E3%83%AB-%E6%96%B0%E5%88%B0%E7%9D%80/%E5%A4%A7%E6%B3%A8%E7%9B%AE-%EF%BC%B6%EF%BC%A9%EF%BC%B3%EF%BC%A9%EF%BC%AF%EF%BC%AE%EF%BC%B1%EF%BC%B5%EF%BC%A5%EF%BC%B3%EF%BC%B4-%E6%96%B0%E5%88%B0%E7%9D%80 バスケットボールＶＩＳＩＯＮＱＵＥＳＴ] the future. I love to ~~read, preferably on my beloved Kindle. I like to watch Grey's Anatomy and The Vampire Diaries as well as docus about nature. I like Fossil hunting.<br><br>Look into my blog :: [http://www.taskrb~~.com/%E5%93%81%E8%B3%AA%E8%87%B3%E4%B8%8A-%E3%82%B8%E3%83%A7%E3%82%AE%E3%83%B3%E3%82%B0%E3%83%BB%E3%83%9E%E3%83%A9%E3%82%BD%E3%83%B3-%E6%BF%80%E5%AE%89%E5%B8%82%E5%A0%B4/%E6%B4%97%E7%B7%B4%E3%81%95%E3%82%8C%E3%81%9F-%E3%83%A6%E3%83%8B%E3%82%BB%E3%83%83%E3%82%AF%E3%82%B9-%E3%82%92%E6%AC%B2%E3%81%97%E3%81%84 ジョギングマラソンユニセックス]		Technical Cable Jointer Tod Falkenstein from Saskatchewan, spends time with hobbies and interests which include beachcombing, [http://www.comoganhardinheiro101.com/inicio/ como ganhar dinheiro na internet] and sky diving. Felt exceptionally encouraged after setting up a journey to Saloum Delta.

en>BG19bot: WP:CHECKWIKI error fix for #61. Punctuation goes before References. Do general fixes if a problem exists. - using AWB (9916)

2014-02-06T09:07:14Z

WP:CHECKWIKI error fix for #61. Punctuation goes before References. Do general fixes if a problem exists. - using AWB (9916)

← Older revision		Revision as of 11:07, 6 February 2014
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	~~{{Multiple issues\|primarysources = May 2012\|~~		I'm a 41 years old, married and work at the high school (Asian Studies).<br>In my spare time I try to teach myself [http://www.taskrb.com/%E5%93%81%E8%B3%AA%E3%81%AF%E9%9D%9E%E5%B8%B8%E3%81%AB%E8%89%AF%E3%81%84-%E3%83%9B%E3%83%83%E3%82%B1%E3%83%BC-%E6%96%B0%E7%9D%80/%E6%9C%89%E5%90%8D%E3%83%96%E3%83%A9%E3%83%B3%E3%83%88-%E3%82%B7%E3%83%A7%E3%83%AB%E3%83%80%E3%83%BC-%E5%8E%B3%E9%81%B8%E3%81%97%E5%88%B6%E4%BD%9C%E3%81%97%E3%81%9F/%E6%96%B0%E3%81%97%E3%81%8F%E7%9D%80%E3%81%8D-easton-%E3%82%A4%E3%83%BC%E3%82%B9%E3%83%88%E3%83%B3-%E6%96%B0%E4%BD%9C/%E7%89%B9%E3%81%AB%E5%A3%B2%E3%82%8C%E3%81%A6%E3%81%84%E3%82%8B-jr-%E3%82%B8%E3%83%A5%E3%83%8B%E3%82%A2-130%E3%80%9C160cm-%E8%B2%A9%E5%A3%B2 ホッケー jr ジュニア 130〜160cm] Korean. I've [http://www.taskrb.com/%E9%AB%98%E5%93%81%E8%B3%AA-%E6%AD%A6%E9%81%93-%E5%B0%82%E5%A3%B2%E5%BA%97/%E5%BD%93%E5%BA%97%E3%81%AE%E8%AA%A0%E5%AE%9F-%E8%A9%A6%E5%90%88%E3%83%BB%E6%BC%94%E8%88%9E%E7%94%A8%E5%93%81-%E3%82%A2%E3%82%A6%E3%83%88%E3%83%AC%E3%83%83%E3%83%88 武道試合演舞用品] been twicethere and look forward to returning sometime in [http://www.taskrb.com/%E6%9C%89%E5%90%8D%E3%83%96%E3%83%A9%E3%83%B3%E3%83%88-%E3%83%90%E3%82%B9%E3%82%B1%E3%83%83%E3%83%88%E3%83%9C%E3%83%BC%E3%83%AB-%E6%96%B0%E5%93%81/%E5%A4%A7%E4%BA%BA%E6%B0%97-%E3%83%9C%E3%83%BC%E3%83%AB-%E5%B0%82%E5%A3%B2%E5%BA%97/%E8%B6%85%E5%AE%89%E3%81%84-%EF%BC%97%E5%8F%B7%E3%83%9C%E3%83%BC%E3%83%AB-%E6%96%B0%E5%88%B0%E7%9D%80/%E5%A4%A7%E6%B3%A8%E7%9B%AE-%EF%BC%B6%EF%BC%A9%EF%BC%B3%EF%BC%A9%EF%BC%AF%EF%BC%AE%EF%BC%B1%EF%BC%B5%EF%BC%A5%EF%BC%B3%EF%BC%B4-%E6%96%B0%E5%88%B0%E7%9D%80 バスケットボールＶＩＳＩＯＮＱＵＥＳＴ] the future. I love to read, preferably on my beloved Kindle. I like to watch Grey's Anatomy and The Vampire Diaries as well as docus about nature. I like Fossil hunting.<br><br>Look into my blog :: [http://www.taskrb.com/%E5%93%81%E8%B3%AA%E8%87%B3%E4%B8%8A-%E3%82%B8%E3%83%A7%E3%82%AE%E3%83%B3%E3%82%B0%E3%83%BB%E3%83%9E%E3%83%A9%E3%82%BD%E3%83%B3-%E6%BF%80%E5%AE%89%E5%B8%82%E5%A0%B4/%E6%B4%97%E7%B7%B4%E3%81%95%E3%82%8C%E3%81%9F-%E3%83%A6%E3%83%8B%E3%82%BB%E3%83%83%E3%82%AF%E3%82%B9-%E3%82%92%E6%AC%B2%E3%81%97%E3%81%84 ジョギングマラソンユニセックス]
	~~{{expert-subject\|date=January 2012}}~~
	}}

	~~In [[statistics]], the ''~~'~~maximal information coefficient (MIC)''' is~~ a ~~measure of the strength of the linear or non-linear association between two variables ''X''~~ and~~ ''Y''.~~

	~~The MIC belongs to~~ the ~~maximal information-based nonparametric exploration~~ (~~MINE~~) ~~class of statistics~~.<~~ref>{{Cite doi\|10.1126/science.1205438\|noedit}}</ref~~> In a simulation study, MIC outperformed some selected functions,<ref name=MIC>Reshef et al. 2011</ref> however concerns have been raised regarding reduced [[statistical power]] in detecting some associations in settings with low sample size.<ref>[http://www~~-stat~~.~~stanford~~.~~edu~~/~~~tibs~~/~~reshef~~/~~comment.pdf Comment on “Detecting Novel Associations in Large Data Sets” by Reshef et al., Science Dec. 16, 2011~~]~~</ref> It is claimed<ref name=MIC/> that MIC approximately satisfies a property called ''equitability'' which is illustrated by selected simulation studies~~.~~<ref name=MIC/> It was later proved that no non-trivial coefficient can exactly satisfy the~~ '~~'equitability'' property as defined by Reshef et al.<ref name=MIC/><ref>~~[http://~~arxiv~~.~~org~~/~~abs~~/~~1301.7745v1 Equitability, mutual information,~~ and ~~the maximal information coefficient by Justin B. Kinney, Gurinder S. Atwal, arXiv Jan. 31, 2013]</ref>~~ ~~Some criticims of MIC are addressed by Reshef et al. in further studies published on arXiv.<ref>~~[http://~~arxiv~~.~~org~~/~~abs~~/~~1301.6314v1 Equitability Analysis of~~ the ~~Maximal Information Coefficient, with Comparisons by David Reshef, Yakir Reshef, Michael Mitzenmacher, Pardis Sabeti, arXiv Jan~~. 27, ~~2013]</ref>~~

	~~== Overview ==~~

	~~The maximal information coefficient uses [[Data binning\|binning]] as a means to apply [[Mutual Information\|mutual information]]~~ on ~~continuous random variables~~. ~~Binning has been used for some time as a way of applying mutual information~~ to ~~continuous distributions; what MIC contributes in addition is a methodology for selecting the number of bins~~ and ~~picking a maximum over many possible grids.~~

	The rationale is that the bins for both variables should be chosen in such a way that the mutual information between the variables be maximal. That is achieved whenever <math>\mathrm{H}\left(X_b\right)=\mathrm{H}\left(Y_b\right)=\mathrm{H}\left(X_b,Y_b\right)</math>.<ref>The "b" subscripts have been used to emphasize that the mutual information is calculated using the bins</ref> Thus, when the mutual information is maximal over a binning of the data, we should expect that the following two properties hold, as ~~much~~ as ~~made possible by the own~~ nature ~~of the data~~. First, the bins would have roughly the same size, because the entropies <math>\mathrm{H}(X_b)</math> and <math>\mathrm{H}(Y_b)</math> are maximized by equal-sized binning. And second, each bin of ''X'' will roughly correspond to a bin in ''Y''.

	~~Because the variables X and Y are reals, it is almost always possible to create exactly one bin for each (''x'',''y'') datapoint, and that would yield a very high value of the MI~~. ~~To avoid forming this kind of trivial partitioning, the authors of the paper propose taking a number of bins~~ <~~math~~>~~n_x~~<~~/math~~> ~~for ''X'' and <math>n_y</math> whose product is relatively small compared with the size N of the data sample. Concretely, they propose~~:

	~~<math>n_x\times n_y \leq \mathrm{N}^{0.6} <~~/~~math>~~

	~~In some cases it is possible to achieve a good correspondence between <math>X_b<~~/~~math> and <math>Y_b</math> with numbers as low as <math>n_x=2</math> and <math>n_y=2</math>, while in other cases the number of bins required may be higher~~. ~~The maximum for <math>\mathrm{I}(X_b;Y_b)<~~/math> is determined by H(X), which is in turn determined by the number of bins in each axis, therefore, the mutual information value will be dependent on the number of bins selected for each variable. In order to compare mutual information values obtained with partitions of different sizes, the mutual information value is normalized by dividing by the maximum achieveable value for the given partition size.
	~~Entropy is maximized by uniform probability distributions, or in this case, bins with the same number of elements. Also, joint entropy is minimized by having a one~~-to-~~one correspondence between bins. If we substitute such values in the formula~~
	~~<math>I(X;Y)=H(X)+H(Y)~~-H(X,Y)</math>, we can see that the maximum value achieveable by the MI for a given pair <math>n_x,n_y</math> of bin counts is <math>\log\min\left(n_x,n_y\right)</math>. Thus, this value is used as a normalizing divisor for each pair of bin counts.

	Last, the normalized maximal mutual information value for different combinations of <math>n_x</math> and <math>n_y</math> is tabulated, and the maximum value in the table selected as the value of the statistic.

	~~==References==~~
	~~{{Reflist}}~~

	~~[[Category:Information theory]]~~
	~~[[Category:Covariance and correlation]~~]

en>Kku: wikified

2014-01-10T13:50:24Z

wikified

← Older revision		Revision as of 15:50, 10 January 2014
Line 1:		Line 1:
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			{{expert-subject\|date=January 2012}}
			}}

			In [[statistics]], the '''maximal information coefficient (MIC)''' is a measure of the strength of the linear or non-linear association between two variables ''X'' and ''Y''.

			The MIC belongs to the maximal information-based nonparametric exploration (MINE) class of statistics.<ref>{{Cite doi\|10.1126/science.1205438\|noedit}}</ref> In a simulation study, MIC outperformed some selected functions,<ref name=MIC>Reshef et al. 2011</ref> however concerns have been raised regarding reduced [[statistical power]] in detecting some associations in settings with low sample size.<ref>[http://www-stat.stanford.edu/~tibs/reshef/comment.pdf Comment on “Detecting Novel Associations in Large Data Sets” by Reshef et al., Science Dec. 16, 2011]</ref> It is claimed<ref name=MIC/> that MIC approximately satisfies a property called ''equitability'' which is illustrated by selected simulation studies.<ref name=MIC/> It was later proved that no non-trivial coefficient can exactly satisfy the ''equitability'' property as defined by Reshef et al.<ref name=MIC/><ref>[http://arxiv.org/abs/1301.7745v1 Equitability, mutual information, and the maximal information coefficient by Justin B. Kinney, Gurinder S. Atwal, arXiv Jan. 31, 2013]</ref> Some criticims of MIC are addressed by Reshef et al. in further studies published on arXiv.<ref>[http://arxiv.org/abs/1301.6314v1 Equitability Analysis of the Maximal Information Coefficient, with Comparisons by David Reshef, Yakir Reshef, Michael Mitzenmacher, Pardis Sabeti, arXiv Jan. 27, 2013]</ref>

			== Overview ==

			The maximal information coefficient uses [[Data binning\|binning]] as a means to apply [[Mutual Information\|mutual information]] on continuous random variables. Binning has been used for some time as a way of applying mutual information to continuous distributions; what MIC contributes in addition is a methodology for selecting the number of bins and picking a maximum over many possible grids.

			The rationale is that the bins for both variables should be chosen in such a way that the mutual information between the variables be maximal. That is achieved whenever <math>\mathrm{H}\left(X_b\right)=\mathrm{H}\left(Y_b\right)=\mathrm{H}\left(X_b,Y_b\right)</math>.<ref>The "b" subscripts have been used to emphasize that the mutual information is calculated using the bins</ref> Thus, when the mutual information is maximal over a binning of the data, we should expect that the following two properties hold, as much as made possible by the own nature of the data. First, the bins would have roughly the same size, because the entropies <math>\mathrm{H}(X_b)</math> and <math>\mathrm{H}(Y_b)</math> are maximized by equal-sized binning. And second, each bin of ''X'' will roughly correspond to a bin in ''Y''.

			Because the variables X and Y are reals, it is almost always possible to create exactly one bin for each (''x'',''y'') datapoint, and that would yield a very high value of the MI. To avoid forming this kind of trivial partitioning, the authors of the paper propose taking a number of bins <math>n_x</math> for ''X'' and <math>n_y</math> whose product is relatively small compared with the size N of the data sample. Concretely, they propose:

			<math>n_x\times n_y \leq \mathrm{N}^{0.6} </math>

			In some cases it is possible to achieve a good correspondence between <math>X_b</math> and <math>Y_b</math> with numbers as low as <math>n_x=2</math> and <math>n_y=2</math>, while in other cases the number of bins required may be higher. The maximum for <math>\mathrm{I}(X_b;Y_b)</math> is determined by H(X), which is in turn determined by the number of bins in each axis, therefore, the mutual information value will be dependent on the number of bins selected for each variable. In order to compare mutual information values obtained with partitions of different sizes, the mutual information value is normalized by dividing by the maximum achieveable value for the given partition size.
			Entropy is maximized by uniform probability distributions, or in this case, bins with the same number of elements. Also, joint entropy is minimized by having a one-to-one correspondence between bins. If we substitute such values in the formula
			<math>I(X;Y)=H(X)+H(Y)-H(X,Y)</math>, we can see that the maximum value achieveable by the MI for a given pair <math>n_x,n_y</math> of bin counts is <math>\log\min\left(n_x,n_y\right)</math>. Thus, this value is used as a normalizing divisor for each pair of bin counts.

			Last, the normalized maximal mutual information value for different combinations of <math>n_x</math> and <math>n_y</math> is tabulated, and the maximum value in the table selected as the value of the statistic.

			==References==
			{{Reflist}}

			[[Category:Information theory]]
			[[Category:Covariance and correlation]]

en>Jayemd at 21:25, 26 July 2012

2012-07-26T21:25:21Z

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Its a sad reality that, in these modern day instances, somewhere in between ten% and 20% of adult girls have never knowledgeable an orgasm, and as numerous as 50% of girls dont orgasm during sex. Should people hate to dig up further on [https://www.youtube.com/watch?v=VjMx1Vkoqgs best sex toy], there are tons of databases you could pursue. Sexual and sensual education have advanced to exactly where we now realize significantly more about the female orgasm, such as the fact that ladies have two entirely diverse areas they can stimulate in order to bring themselves to complete satisfaction. Considering that the vibrator was specifically developed to enable women to knowledge orgasms, it remains the very best tool for females to discover what they appreciate, and what stimulates them in the best way to obtain the ultimate O. The initial step, of course, is acquiring a vibrator. Most adult shops are fairly cheesy and uncomfortable for females to shop in, which is why the web is such a common medium for adult toy buying. Appear at the photographs, read about the materials, and pick anything you locate appealing and that looks enjoyable. This is the very first step on a fantastic journey, so enjoy it! To have the greatest encounter with your vibrator, give oneself some time with it. Prior to you even switch it on, get relaxed and turned on. Take a extended bubble bath, read an erotic story, get your self in the mood. Going To [https://www.youtube.com/watch?v=iSsW59NcDPo screaming o vibrating ring] seemingly provides aids you should use with your brother. For most women, orgasms are as significantly psychological as they are physiological. If your mind is not in the right location, your body will not be either. Get comfy on your bed, on a rug in front of your fireplace, or just stay in the tub if youve purchased a waterproof vibe. Loosen up, commence slow, and get to know your vibrator and your body. Touch diverse parts of your genitals with your vibrator. Uncover what feels good, then maintain performing it. Let your arousal build and let nature take its course. There actually isn"t any "correct" way to use a vibrator or to bring oneself to orgasm. The most critical factor to don"t forget is that a vibrator is a tool to help you stimulate oneself whilst it can help you attain a climax, it"s not an instant orgasm machine. You manage it and use it in the way that feels very best to you. Experiment with your vibrator, try its different features, and apply it to various locations of your physique to see what the sensations are like. Most girls respond to clitoral stimulation, but you might favor far more or much less intensity, or a lot more or much less direct stimulation. The labia and vulva are also sensitive. If your vibrator is insertable, give that a try. Some girls uncover penetration and vibration inside the vagina very pleasurable. A vibrator is the best tool you can use to locate and stimulate your G-spot. This tiny node of pleasure is on the front wall of your vagina, a couple of inches in. It can take a handful of tries to locate this spot, and not everyone who finds it in fact likes it simply because of how sensitive it can be. Some females can not discover it at all. All of these items are regular, so you just have to see what works for you. A lot of girls want clitoral rather than vaginal stimulation to in fact bring them to orgasm, so if vaginal penetration with your vibrator is not getting you anyplace, go back to making use of the vibrator on your clitoris. One particular technique you can use with a smooth insertable vibrator is to location the tip against your clitoris, then slide the shaft down among your labia, insert the vibrator and slide it into your vagina, and then bring it back up in a reverse stroke, sliding along the clitoris once more on the way up. Once you learn what feels very good, keep carrying out it, loosen up, and let your arousal develop. If you find that the vibrator brings you to the point of climaxing too rapidly, back off and try a much less intense form of stimulation, or even use your hand for a even though and go back to the vibrator. Youll find that, like a lot of factors, the a lot more you practice achieving orgasm, the simpler itll become. Mastering how to orgasm throughout sex is entire various thing. A single of the very best things you can do is simply to masturbate typically and hold all of your sexual organs in great shape. 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