Episode #125 of the Stack Overflow podcast is here. We talk Tilde Club and mechanical keyboards. Listen now
Search type Search syntax
Tags [tag]
Exact "words here"
Author user:1234
user:me (yours)
Score score:3 (3+)
score:0 (none)
Answers answers:3 (3+)
answers:0 (none)
isaccepted:yes
hasaccepted:no
inquestion:1234
Views views:250
Sections title:apples
body:"apples oranges"
URL url:"*.example.com"
Favorites infavorites:mine
infavorites:1234
Status closed:yes
duplicate:no
migrated:no
wiki:no
Types is:question
is:answer
Exclude -[tag]
-apples
For more details on advanced search visit our help page
Results tagged with Search options answers only user 805

Probability density function (PDF) of a continuous random variable gives the relative probability for each of its possible values. Use this tag for discrete probability mass functions (PMFs) too.

6
votes
If you don't write down the support, you may not see what's going on -- but as soon as you do, it's a lot clearer. I am not able to understand the difference between the joint density function and …
answered Feb 14 '16 by Glen_b
0
votes
give a complete definition of the pdf; it's a trivial matter to convert to my original $(0,\theta)$ case (scaling the width by $\theta$ and the height by $1/\theta$) and then to shift to your own … example. (Or you could employ the same strategy on your original example directly -- but I am very much in the vein of converting already-solved problems when I can.) More complicated pdf examples may in …
answered May 14 by Glen_b
10
votes
least four papers). One example is Figure 1 of Dunn & Smyth (2001) "Tweedie Family Densities: Methods of Evaluation", Proceedings of the 16th International Workshop on Statistical Modelling, Odense, Denmark, 2–6 July. (pdf preprint). It's not such a problem if everyone is clear what they're looking at] …
answered Mar 20 '18 by Glen_b
4
votes
In the univariate case, one quick approximation: You could take a moderate number of bins (in the univariate case, say something on the order of a thousand, though it depends on your bandwidth - you n …
answered Nov 19 '14 by Glen_b
1
vote
either the cdf or the pdf for $M$ is straightforward and can essentially be done from first principles. (See wikipedia on truncated distributions) The approach for other distributions is similar …
answered Aug 24 '16 by Glen_b
1
vote
When you specify freq=FALSE area under the histogram represents proportion of the data (i.e. the histogram is a density estimate). The total area is 1. So the height is not estimating probability, th …
answered Nov 5 '16 by Glen_b
1
vote
]=\int_{-\infty}^\infty (x-\mu)^k f(x) dx$ See Wikipedia on Moments (mathematics). Given a pdf and the values of the parameters, can we calculate the moments of the distribution? If we can evaluate … squared. Yes, using basic properties of expectation, you can write $E[(X-\mu)^2]=E[X^2]-\mu^2$. See Wikipedia on variance. Is this an integral over all the support of the pdf? Strictly the …
answered Jul 18 '16 by Glen_b
4
votes
As discussed in Dilip's response here, you can take the approach of doing direct integration with the bivariate density; however, I want to mention that while this is perhaps the most straightforward …
answered Nov 5 '14 by Glen_b
2
votes
There are a number of density estimators that are piecewise constant. The scaled histogram is the most obvious: (the scaling is to make the total area 1, so that it estimates the underlying densit …
answered Mar 27 '15 by Glen_b
2
votes
What convinces you that the Gaussian copula only applies to linear correlation? This would seem to be a counterexample: a pair of variables with a Gaussian copula, but they're not linearly related: …
answered Jan 2 '14 by Glen_b
5
votes
better motivation. Imagine we're dealing with a sufficiently "nice" pdf, say one that's Lipschitz continuous. Then in a direct sense, as the interval in $x$ gets smaller, the interval in $f$ must also get …
answered Jul 17 '15 by Glen_b
8
votes
If we have a variable $X\sim U(0,1)$ and multiply it by $a$, then $aX\sim U(0,a)$. Assume that we're dealing with independent continuous uniform on $(0,a)$ and $(0,b)$ respectively (with $a<b$) (Thi …
answered Aug 5 '16 by Glen_b
2
votes
The mean and variance are not actually finite in either the Gaussian nor the Laplace case. You can try to use a Taylor expansion as they did in the paper you mention, but for that to actually be corr …
answered Jul 22 '16 by Glen_b
2
votes
Well the pmf looks something like the second of the two plots below:
answered Oct 26 '15 by Glen_b
2
votes
Indeed, even all (positive integer) moments may in some situations be insufficient to distinguish two distributions. Generally, the first few moments don't 'pin down' a distribution very well, which s …
answered Feb 26 '15 by Glen_b

15 30 50 per page