Talk:Exponential distribution
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[edit] Exponential
I understand that the infobox text reads "Name: Exponential" and that makes sense, but what is displayed is just "Exponential" and that makes no sense, its an adjective thats missing a noun to modify. Alternatively, we could change the infobox to display "{{{name}}} distribution"? PAR 10:31, 1 Apr 2005 (UTC)
- Take the discussion here please: Template talk:Probability distribution Cburnett 15:04, 1 Apr 2005 (UTC)
[edit] Rayleigh & Expo fishyness
Something's fishy about the formula relating the Raleigh and the exponential distribution. The parameters λ and β have to be related somehow. 141.140.6.60 22:36, 7 May 2005 (UTC)
- That's right. I will fix it right now. Also, you seem to know this subject. How about some help with the other Category:probability distributions? We could sure use it. We are trying to put full infoboxes in all of them. PAR 23:57, 7 May 2005 (UTC)
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- Yup, I'll continue to do little things here and there. I forgot to log in for the edits to this article. AxelBoldt 00:53, 8 May 2005 (UTC)
- I got the relationship from Statistical Inference by Casella & Berger (ISBN 0534243126) but it I don't think it gave the parameter relationship (it might be lambda = beta) but I'd have to pull out some paper and confirm by transforming it. Unfortunately, I'm in the middle of moving and it's boxed up. Cburnett 06:36, May 8, 2005 (UTC)
[edit] Email query
From an email to the Foundation:
"Could [you] explain why the Exponential distribution can be generated by taking the natural log of the uniform distribution? Is there any analytical proof? The author cited the quantile function, but it is not clear how. Thanks.
The part of the article quoted was:
"Given a random variate U drawn from the uniform distribution in the interval (0,1], the variate has an exponential distribution with parameter £f. This follows from the form of the quantile function given above and yields a convenient way to produce exponentially distributed values using a random number generator on a computer, for instance to conduct simulation experiments."
I've directed the correspondent here - can anyone help? Thanks -- sannse (talk) 19:33, 15 January 2006 (UTC)
- This may be the wrong place. I've added a pointer to inverse transform sampling method here, and the (rather trivial) formal proof for the general case there. --MarkSweep (call me collect) 23:37, 15 January 2006 (UTC)
[edit] Comment
Sorry if I'm doing this wrong, I'm a wikipedia newbe. I just wanted to mention that I got confused for a while in the Bayesian inference section because the Gamma distribution is not parameterized the same way as on the Gamma distribution page. I'm not an expert, there might be a good reason for this which is over my head. If not, it would probably be nice to change it to make it more consistant with the gamma distribution page so that the resulting likelyhood is
--BenE 19:20, 14 July 2006 (UTC)
[edit] Confusion with alternative spec.
Well, the text takes non-alternative spec., but I see at least the last two formulas of Related distributions are using alter. spec. So, I have modified them to non-alter. spec. Ping my talk page if you need a discussion. Thanks. --Amr 12:04, 1 September 2006 (UTC)
[edit] Occurrence and applications - Gamma distribution
"In queueing theory, the inter-arrival times (i.e. the times between customers entering the system) are often modeled as exponentially distributed variables. The length of a process that can be thought of as a sequence of several independent tasks is better modeled by a variable following the gamma distribution (which is a sum of several independent exponentially distributed variables)."
This is quoted from the current page. Isn't the exponential distribution just a special case of the gamma distribution (alpha=1, beta=1/lambda)? If so, it doesn't make sense to me to say that gamma distributions are better when the exponential is itself a gamma distribution. Should this section of text be changed or clarified? --JRavn talk 22:09, 7 February 2007 (UTC)
