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Theory and applications of probability and stochastic processes: e.g. central limit theorems, large deviations, stochastic differential equations, models from statistical mechanics, queuing theory.
1
vote
Accepted
Probability of at most $K$ consecutive zeroes in a sequence of 0s and 1s
Let $a<1<b$. I prove that for large $n$:
1) the probability that a random sequence of length $n$ has at least $B:=b\log_2 n$ consecutive zeroes is at most $n^{1-b}$;
2) the probability that a rando …
3
votes
Accepted
Is $\mathbb E\left[\frac{d}{||x||_1^2}\right]=O(1)$ for all $d\in\mathbb R^+$, where $x\in S...
We may sample $x$ as follows: choose i.i.d. standard Gaussian $\xi_1,\ldots,\xi_d$ and put $$x_i=\frac{\xi_i}{\sqrt{\sum_{j=1}^d \xi_j^2}},\quad i=1,2,\ldots,d.$$
Then
$$\frac1{\|x\|_1^2}=\frac{\sum \ …
5
votes
Accepted
Estimation of the expected number of sites visited by i.i.d
Denote $p_k=P(X=k)$. Then $E(R_n)=\sum_k P(k\in \{X_1,\ldots,X_n\})\leqslant \sum_k \min(1,np_k)$. We are given that $\sum kp_k<\infty$. Fix $\varepsilon>0$. The sum of $\min(1,np_k)$ over $k<\varepsi …
4
votes
A strange probability inequality
Every $\gamma>0$ seems ok. We have $$
\mathbb{P}(|x_k|\geqslant e^{\gamma k})\leqslant
\mathbb{P}(1+|x_k|\geqslant e^{\gamma k})=
\mathbb{P}(\gamma^{-1}\log(1+|x_k|)\geqslant k),
$$
and the sum of th …
7
votes
A limit obtained from a probability distribution on the positive integers
This result belongs to P. Erdös, W. Feller, and H. Pollard (1949). It worth mention that if $\sum a_n p_n<\infty$, this follows from Wiener division theorem in the algebra of absolutely summable Fouri …
4
votes
Accepted
Quantitative Borel-Cantelli
Imagine that for all $k=1,2,\ldots$ all events $A_n$ for $n\in [k!,(k+1)!-1)$ are the same event $C_k$. Then if $x$ belongs to all $A_i$ along a subsequence of positive density yields that it belongs …
0
votes
Speed of convergence in Lebesgue's density theorem
For reals $x<y$ denote $\rho(x,y)=1/2^n$ if integer $n$ is minimal such that there exist integer $i$ with $x<i/2^n\leqslant y$. We have $\rho(x,y)\geqslant |x-y|/2$ and ``in average'' $\rho(x,y)$ beha …
3
votes
Accepted
Expected Size of Independent Set
[Previous answer was completely wrong.]
Denote by $p_k$ the probability that at least $k$ vertices remain. Then the expectation is of course $\sum p_i$. I claim that $p_k=\frac{(n-k+1)! (n-k)!}{(n-2k …
4
votes
Dice waiting time for $5,6$ is smaller than for $6,6$
Compare 56 and 55 instead. Denote $a=6$ in the first case and $a=5$ in the second, $b=11-a$. Look at the first 5. If the next term equals $a$, we are done (the same thing for both cases). If the next …
1
vote
Expected number of crossings of the diagonal of a lattice path?
I count $(0,0)$ and $(n,n)$ as crossings, subtract 2 if you do not want. For $k\in \{0,1,\dots,n\}$ denote by $\xi_k$ the random variable which equals 1 if $(k,k)$ lies on the path and 0 otherwise. Th …
6
votes
Accepted
Inequality with ratio of Normal CDFs
I claim that even $$\frac{\Phi(-\theta)}{\Phi(-\lambda \theta) }< e^{\frac{\theta^2(-1+\lambda^2)}{2}}.$$
Rewrite this as $h(\theta)<h(\lambda \theta)$, where $h(\theta)=\Phi(-\theta)e^{\theta^2/2}$. …
3
votes
Accepted
Probability space analogue of Cauchy-Schwarz inequality
yes, if $\sum_{i,j\in A}$ means double summation (each pair $i,j$ is taken twice)
then denote $f_i$ and $g_i$ characteristic functions of your events from $A$ and $B$ respectively, LHS equals $2 \sum …
5
votes
Accepted
Product of coordinates of a random point from Hamming sphere
If I am not mistaken, this expectation equals the coefficient of $x^{\varepsilon n}$ in $(1-x)^{\alpha n}(1+x)^{(1-\alpha) n}$ divided by the corresponding coefficient in $(1+x)^n$ (which equals, of c …
2
votes
Accepted
The size of monochromatic submatrix
Even $c=1-\varepsilon$ works when $n$ is large enough. This question is about bounding the diagonal bipartite Ramsey, see the recent achievement of David Conlon here
http://people.maths.ox.ac.uk/~conl …
1
vote
Accepted
Sampling with non-uniform probabilities
Usually not. Denote $f_i=\mathbb{1}_{i\in S}$, then $\mathbb{E} f_i=\mathbb{E} f_i^2=kp_i$, $\sum f_i\equiv k$, $\mathbb{E} f_if_j=cp_ip_j$ if $i\ne j$. Thus
$$k^2p_i=k\mathbb{E} f_i=\mathbb{E} \sum_j …