User talk:O18

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Talkback[edit]

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Hello, O18. You have new messages at Wikipedia_talk:WikiProject_Libraries#librarians_in_fiction.
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Hi, not sure if this is proper, but thought I'd give it a shot and see what happens. I was struck by your user name and wondered if you were referring to the element oxygen. If so, I'd like to suggest that you use "18O" (with the 18 as superscript). This would be the correct chemical notation, and it would also be a play on the "do a 180" (as in 180 degrees). Anyway, I don't know if this message will actually get to anyone, much less the person intended, but I figured I'd give a shot at the "talkback" to see what transpires... Jdevola (talk) 18:46, 28 March 2013 (UTC)

O18 was correct in the early 20th century, I think. Maybe that's what he was aiming at? Double sharp (talk) 13:29, 25 December 2013 (UTC)

WikiProject Economics census[edit]

Hello there. Sorry to bother you, but you are (titularly at least) a member of WP:WikiProject Economics, as defined by this category. If you don't know me, I'm a Wikipedia administrator, but an unqualified economist. I enjoy writing about economics, but I'm not very good at it, which is why I would like to support in any way I can the strong body of economists here on Wikipedia. I'm only bothering you because you are probably one of them. Together, I'd like us to establish the future direction of WikiProject Economics, but first, we need to know who we've got to help.

Whatever your area of expertise or level of qualification, if you're interested in helping with the WikiProject (even if only as part of a larger commitment to this wonderful online encyclopedia of ours), would you mind adding your signature to this page? It only takes a second. Thank you. Message delivered on behalf of User:Jarry1250 by LivingBot.

United States public debt[edit]

This is where I got the US Federal debt as % of GDP numbers - http://www.usgovernmentspending.com/federal_debt_chart.html; and the Tax brackets are here - http://www.ntu.org/tax-basics/history-of-federal-individual-1.html. I don't have the time, was just trying to help out... Geek2003 (talk) 12:29, 13 October 2010 (UTC)


I've drafted a Wikipedia article: User:Csdidier/Public_Debt_Vocabulary_Shift. I was wondering if you would be willing to give it a quick look, and offer some criticism.Csdidier (talk) 16:06, 26 November 2010 (UTC)

Bayesian stuff[edit]

I really think you're making way too big a deal out of something that is really not a very big deal. If you make a statement like "There is a 95% probability that between 36% and 44% of the voters want to vote for the party in question." then obviously it depends on the assumptions you've made in your model. But this is the case with every scientific statement about anything. There is nothing in Bayesian statistics that's any more subtle than what applies to any scientific discipline. The point I'm trying to make in the article on confidence intervals is a very simple one: Bayesian statistics provides you a model for making probabilistic statements about uncertainty due to lack of knowledge, while frequentist statistics does not. This is something that's important for a non-expert reader to know. You seem to think it's better not to state this at all than to make a point that isn't completely, utterly technically correct. I'm actually rather surprised by this, as in general you seem to share my viewpoint that Wikipedia articles need to be addressed to the non-expert reader rather than to the expert reader -- an excessive focus on technical issues is exactly what makes Wikipedia articles overly complicated and unreadable. Rather than constantly reverting my changes because you find some small fault in them, please try to be constructive and figure out how better to express the basic issue! If you don't like the way I've phrased the reason why people might use frequentist statistics instead of Bayesian statistics, then help me find a better way of phrasing it. Is it enough to say that the use of Bayesian inference "requires additional, subjective assumptions that may limit the circumstances in which the conclusions can be applied"? The point is to try and educate the lay reader on what the major issues and alternatives are, not to provide a cookbook explaining exactly how and when the alternatives should be applied; honestly I think it's plenty enough simply to note that there are additional complexities or subtleties in Bayesian statistics that lead some to favor frequentist statistics despite its limitations, without needing to state what those complexities are. Benwing (talk) 01:13, 20 October 2010 (UTC)

The fact that the interpretation of confidence intervals is not what people think is not a "precise, technical" issue, it's a fundamental fact of confidence intervals. This is not at all the same as the fact that Bayesian results are inapplicable if your model assumptions are false, which is a basic fact of all scientific results. I think it's important to address the important points in an article, which includes a discussion of alternatives. Nonetheless, I'm going to leave this article alone for a couple of days and revisit it later. When I do, rather than start an RFC, I'll follow WP:BRD; I believe this to be more effective, as I've found that there is little incentive to address concerns unless positive action (e.g. revert) is required to do so. Benwing (talk) 02:37, 21 October 2010 (UTC)
OK, I get the feeling I'm not quite understanding what you're saying. When you say "I'm saying that when you present Pr(A|B,C) you had better make sure that the B and C are in the statement", can you elaborate what you mean by B and C? Do you mean the assumptions you've made about your prior distribution, and other assumptions that go into your model? Or is there something else you're referring to? If it's the former, would you be satisfied by something like "In the case of the voting poll described above, assuming that voters have made up their minds, the uncertainty about the true percentage of votes for the party in question is due to the fact that the choices of voters who have not been polled is unknown, rather than due to any actually random behavior on the part of the voters. The paradigm of frequentist statistics does not allow for probabilistic reasoning about uncertainty of this sort, due to lack of knowledge rather than objectively random events. Bayesian statistics does allow for such reasoning; however, it requires significant additional assumptions about the unknown quantities that can be difficult to justify objectively, and without which the results are meaningless." This makes very clear the respective limitations of the different approaches. I don't explicitly mention the model assumptions other than those related to the prior distribution, but these are going to apply to both approaches and in fact to any scientific model. Now in point of fact, the frequentist approach does make assumptions about the prior distribution, specifically that it's uniform; but that is a separate issue. (Part of the reason I'm skeptical about frequentist statistics is that in my line of work, which is natural language processing, assuming a uniform prior is very often drastically wrong and produces completely nonsensical results.) Benwing (talk) 05:01, 21 October 2010 (UTC)
Thanks for continuing to engage me and try to find consensus; I really appreciate that. I'm aware that I'm not always the most diplomatic of people, esp. when I get frustrated.
The reason I want to say something to the effect of what I wrote just above is that I think the issue of what confidence intervals actually mean is a pretty basic fact that needs to be discussed in a way that a non-expert will understand. I do see that there are two sections about Bayesian alternatives, but the more useful section (the first section under "Alternatives", before "Philosophical issues") is way down near the bottom. Here's a suggestion for what I'd like to do:
  • In "Introduction", put back some of the text I wrote that discusses the trickiness in interpreting the results, without the Bayesian reference.
  • In the lead, take out the sentence "Confidence intervals are used in frequentist statistics; the equivalent in Bayesian statistics is the credible interval." and instead, put an extra paragraph at the end of the lead that says:
Confidence intervals are used in frequentist statistics. The equivalent in Bayesian statistics is the credible interval. Bayesian statistics does provide a method for computing the probability that the true parameter value lies in a given interval, but comes with its own issues and limitations; see below for more information.
Also, in the "Alternatives" section, is the following statement about confidence intervals actually true?
"Users of Bayesian methods, if they produced an interval estimate, would in contrast to confidence intervals, want to say "My degree of belief that the parameter is in fact in this interval is 90%,"[9] while users of prediction intervals would instead say "I predict that the next sample will fall in this interval 90% of the time."[citation needed]"
If you are basically OK with this, then I might go ahead and make these changes and let Melcombe revert if he wants (WP:BRD style). Based on my past dealings with Melcombe, it appears he doesn't like me very much and would rather simply obstruct me than engage in a genuine dialog, as you've been doing. Benwing (talk) 03:24, 22 October 2010 (UTC)
I forgot to answer your question about prior distributions. AFAIK maximum likelihood can be described as frequentist, and maximum likelihood is exactly equivalent to maximum a posteriori with a (possibly improper) uniform prior distribution. In many NLP applications you need a highly non-uniform prior in order to get decent results. For example, in a topic model it is not unusual to use prior distributions that are symmetric Dirichlet distributions with a concentration parameter of 0.001 or even 0.0001; this is because a "topic" is technically a distribution over however many words are in your vocabulary, and if you have a vocabulary of 1,000,000 words (not uncommon if you construct your vocabulary based on a large text corpus), you absolutely do not want your topic smeared more or less equally over all 1,000,000 of these! Even worse, imagine that you have a corpus consisting of parse trees over 40,000 sentences, and you want to learn a tree substitution grammar from this corpus, which is kind of like a context free grammar (CFG) but where instead of just having rules that expand a single non-terminal parent into its children, your rules can be arbitrarily-sized, anywhere from a simple CFG rule to a rule that expands the entire sentence at once. If you apply expectation maximization (EM) to this problem without some prior distribution that favors small rules, your EM algorithm will come back saying, "OK, I learned 40,000 rules, each of which expands an entire sentence, and by the way I did a really good job, since p(data|rules) = 1". This is why most computational linguists are committed, die-hard Bayesians.Benwing (talk) 08:11, 22 October 2010 (UTC)
At the same time I totally understand how not everyone is so enamored of having to choose a prior distribution. But what about using non-informative priors? What happens e.g. in the case of the vote poll example, if you do a Bayesian analysis and use a non-informative prior? Presumably you still get out a credible interval. Benwing (talk) 08:11, 22 October 2010 (UTC)
OK, you made a lot of interesting points but didn't actually respond to anything I mentioned regarding the page. As I said, I'm trying to establish consensus with one person at a time, which I think will improve the signal-to-noise ratio. BTW as for your air-traffic example, the short answer is that specific info like "altitude from the ground" doesn't go into the prior at all; rather, it goes into the features. Priors in Bayesian modeling in NLP are only used to express general biases like prefering sparse solutions or perhaps biasing towards high-info or low-info words (you might have a mixture model with separate components for high-info and low-info words). The real reason why voice-recognition currently isn't reliable enough is that it's a really really tough problem that requires much more sophisticated models than we currently have. In fact most production-level systems that suck so badly aren't Bayesian at all, but are just doing basic EM to learn. Bayesian models using MCMC and Dirichlet processes and such tend to be more accurate but they're very new, still at the forefront of research. Benwing (talk) 22:36, 22 October 2010 (UTC)
If our conflict is "irreducible" then it basically means you're going to obstruct anything I suggest so there's no point in talking on the talk page. In such cases I would honestly rather just make the changes I want and force you to revert; at least then there is a possibility of finding an actual consensus. Benwing (talk) 07:21, 23 October 2010 (UTC)
Wikipedia is not a democracy so 2 against 1 is not a valid reason for doing or not doing something. Nonetheless I'm going to leave this alone for now as I'm busy, but when I have time I'll go ahead and make my edits; perhaps you will be surprised after all. Benwing (talk) 00:41, 24 October 2010 (UTC)

Debt[edit]

Sorry, but your revert on the Obama increase of debt in 2010 does not match the figures shown. All the figures (percentage point increases) are calculated from the adjusted debt (Dx) numbers by dividing current year by previous year and substracting 1 (i.e. 100%), e.g. % = (Dn/Dn-1)-1. There is effectively no way how the Obama increase could be around 6%, an should be above 30% (exactly 31.6%). The definition of percentage point does not change anything on this, just read through the article on percentage point increases, which you have added to the heading. — Preceding unsigned comment added by 145.62.32.131 (talk) 16:46, 15 July 2011 (UTC)

Your old talk page[edit]

So just to clarify, you wanted to change your name for privacy purposes? -- Atama 19:59, 15 August 2011 (UTC)

Yes. 018 (talk) 20:07, 15 August 2011 (UTC)
I will delete it then, because that actually is one of the exceptions where we will delete a user talk page. I'll do so now, thank you. -- Atama 20:19, 15 August 2011 (UTC)
Okay, thanks for helping with me. 018 (talk) 20:25, 15 August 2011 (UTC)

Adding R-bloggers.com to the R (programming language) article[edit]

Hello O^{18}

I have revisited the thread we started on:

http://en.wikipedia.org/wiki/Talk:R_(programming_language)#external_links_2

And I would like you to reconsider adding R-bloggers.com to the external links section in the R (programming language) article

Since our last correspondence, R-bloggers has grown to have over 5000 subscribers and over 280 bloggers. The site now hosts the RUG blog which hosts the videos from R user group meetings as well as from useR conferences. Since this is the only resource which offers the most updated view of the rapidly changing world of R, I think it should be reconsidered as a recognized resource for the R world.

I would be happy to hear your thoughts on the matter.

Best, Tal Galili (talk) 07:31, 10 November 2011 (UTC)

It might make more sense to make an article about R-bloggers, if it meets the notability criteria. This page would then like to the official site and could be wikilinked from the R page. Do you believe that it meets the notability guidelines? 018 (talk) 16:24, 10 November 2011 (UTC)
Hi 018,
That is a very interesting direction I did not think about - thank you for your insight!
Making a quick google search, there are over 190K hits for "r-bloggers". The site is linked to from many of the major R sites online. Just to compare, there are 186K hits for ggplot2 and 111K for "sweave" (both have articles).
Yet, I am not sure how much can be written about R-bloggers as an encyclopedic article.
Do you have any suggestion for a similar wiki article which might serve as a blueprint?
Thanks again, Tal Galili (talk) 18:21, 10 November 2011 (UTC)
I'm not sure the number of hits matters. For example, I looked at your linked results and they included pages with advertisements for r-blogger... probably not the best measure of "mind-share." I would look for what makes r-blogger notable (according to Wikipedia) and talk about that. 018 (talk) 18:34, 10 November 2011 (UTC)
I will look into it and get back to you - thanks again. Tal Galili (talk) 18:47, 10 November 2011 (UTC)
Hello dear O18.
I would like to re-visit our discussion regarding the inclusion of r-bloggers.com on the R (programming language) article. The r-bloggers site is much larger now, including thousands of articles on R, with over 400 bloggers, and over 11000 readers. It is mentioned in many websites across the web, and also in books on R (see for example [1]). Do you think it might have matured to the level of significance?
With regards, Tal Galili (talk) 19:39, 4 February 2013 (UTC)
I think current editors of that article are best to decide this. Thanks for the notice! 173.13.207.65 (talk) 23:26, 4 February 2013 (UTC)

file listed for deletion[edit]

File:TnormCDF.png listed for deletion[edit]

A file that you uploaded or altered, File:TnormCDF.png, has been listed at Wikipedia:Files for deletion. Please see the discussion to see why this is (you may have to search for the title of the image to find its entry), if you are interested in it not being deleted. Thank you. Michael Hardy (talk) 05:47, 22 November 2011 (UTC)

Invitation to comment at Monty Hall problem RfC[edit]

You are invited to comment on the following probability-related RfC:

Talk:Monty Hall problem#Conditional or Simple solutions for the Monty Hall problem?

--Guy Macon (talk) 17:20, 8 September 2012 (UTC)

GA Thanks[edit]

Symbol support vote.svg This user helped promote the article Jim Thome to good article status.

On behalf of WP:CHICAGO, I would like to thank you for your editorial contributions to Jim Thome, which has recently become a GA. --TonyTheTiger (T/C/BIO/WP:CHICAGO/WP:FOUR) 01:11, 22 January 2013 (UTC)

File:Silvershiner.jpg[edit]

FYI, there's a problem with your file, File:Silvershiner.jpg, they don't think your permission is valid. -- 65.94.79.6 (talk) 09:04, 23 June 2013 (UTC)