please delete me

Mar
28
awarded Yearling
Mar
28
awarded Yearling
Feb
20
comment Expected wait time for multiple near-simultaneous failures
days${}{}{}{}{}$
Feb
19
answered Expected wait time for multiple near-simultaneous failures
Feb
18
comment Expected minimum of a finite random walk.
for a mean 0 random walk the minimum is not finite, and in general it is hard, and probably unsatisfactory, but you could find a discussion in Spitzer's random walk book, or, I think, Feller Vol 2.
Feb
14
comment Deconvolution of sum of two random variables
you have an identification issue since if e.g. X,Y normal mean 0 the result is normal, mean 0 variance $\sigma^2( 1 + c^2)$, so more than 1 $c, \sigma$ pair can correspond to the dist. of Z.
Feb
13
comment How to find a function in $ L_{2}(0,\infty)$ but not $L_{q}(0,\infty)$, $q\neq 2 $
do the cases q > 2 and q < 2 seperately and add the results.
Feb
7
answered Cutting time out of a Poisson process
Feb
4
comment $\mathbb L^1 +$ a.s. convergence of sequence $(X_n)$ does not imply $\sup(x_n)$ is integrable
the typesetting on that last bit is not what I intended, but I'll leave it unless it confuses you.
Feb
4
answered $\mathbb L^1 +$ a.s. convergence of sequence $(X_n)$ does not imply $\sup(x_n)$ is integrable
Feb
4
comment Exponential integral in the limit $ \epsilon \to 0 $
make change of variables $y = \epsilon x$
Feb
3
comment Relatively compact subspace exercise.
you've left out p, which i believe goes here:$\int_0^1|f'(t)|^pdt$
Feb
3
comment $L\log L$ and $L^p$ embedding
"Does this embedding hold on for instance, the whole of R n , " $\rightarrow$ Does this embedding hold on for instance, the whole of R n , with lebesgue measure . It does not. In $\mathbb R^1$. e.g., take any sequence $a_n > 0$ that is square summable but not LlogL summable, $a_n = 1/n$ will do. Then take the step function equal to $a_n, n < x < n+1$ and $0$ elsewhere.
Jan
30
comment Cardinality of random points in the real number line
this is probably not what you have in mind: run a two state markov process with stationary initial distribution $= (\frac 12, \frac 12)$. Flip a coin to decide what stat you start in, run the process forward & backward, and label the real according to the state of the process. I'm guessing you want something more like i.i.d. labelling.
Jan
30
comment Random determinant problem
the expected determinant is a linear combination with positive weights of determinants each of which is increasing, this is literally true of the distribution of the random guys has finite support, and is true in the sense of being a limit of such guys, or an intergral of them, if you prefer.
Jan
30
comment Proving Linear Independence of Gaussian Functions
in case your still interested, suppose they are ordered by decreasing $\mu_i$. It is east to show that $\lim_{x \rightarrow \infty} e^{x^2}e^{-2 \mu_1 x} \times$ your function $ = a_1e^{\mu_1^2}$ which then has to be 0.
Jan
29
comment Proving Linear Independence of Gaussian Functions
multiply by $e^{x^2}$ and consider growth rate as $x \rightarrow \infty$. It will be determinined by largest $\mu_i$, whose coefficient must therefore be 0. Get rid of it & repeat.
Jan
28
answered Random determinant problem
Jan
27
comment How prove this $\frac{1}{b_{k+1}b_{k+2}}+\frac{1}{b_{k+2}b_{k+3}}+\cdots+\frac{1}{b_{2k}b_{2k+1}}>\frac{1}{12345},$
I'm guessing first use arithmetic/geometric then convexity of $1/x^2$ then your idea.
Jan
25
comment Random determinant problem
I'd be happy too, but what does $^H$ mean ? No sense in running off if its not what I'm guessing.
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