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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.
7
votes
The expected value of product of random variables which have the same distribution but are n...
The answer to the first question is positive, and the lower bound is achieved, since the set of all probability measures on $[0,1]^k$ with uniform marginals is compact and since the integral of the bo …
4
votes
Accepted
Enlargement of filtration
I think that I have a counterexample. Let $(X,Y)$ be a Brownian motion in $\mathbb{R}^2$. Then $M = \int_0^\cdot X_s \mathrm{d}Y_s$ is a martingale, in the natural filtration of $(X,Y)$, in its own fi …
4
votes
Accepted
Integral of $M^\text{*} - M$ with respect to $M^\text{*}$ is zero for $M^\text{*}$ the runni...
The integral with regard do $\mathrm{d}M^*$ is a pathwise Stieltjès integral, so the question is an analysis problem.
Let $f : \mathbb{R}_+ \to \mathbb{R}$ be any continuous function, $F$ its current …
4
votes
Accepted
A convergence problem
I assume that $(a_n)_{n \ge 1}$ are random variables taking values on a finite subset $B$, and that $\nu_l(b) \le P[a_n = b|a_1,\ldots,a_{n-1}] \le \nu_u(B)$ almost surely for every $n \ge 1$ and $b \ …
3
votes
Accepted
Equivalence of unions in probability theory
Let $\epsilon>0$ and $n \ge 1$. Then
$$\bigcap_{k=1}^\infty\{|S_{n+k}-S_n|<\epsilon = \bigcap_{j \geq n}\{|S_j-S_n|<\epsilon\} \subset \bigcap_{j,k\geq n}\{|S_j-S_k|<2\epsilon\} .$$
Hence, taking comp …
3
votes
Conditional expectation: commuting integration and supremum
ADDENDUM. Vokram told me that I answered another question, not his question. Therefore, I give another counterexample (not so different from the previous one) disproving the equality.
Choose a functio …
3
votes
Another curious martingale
Partial answer. Continuous (local) martingales are time-changed Brownian motion.
A way to obtain funny local martingales which also are Markov processes is to start from a Brownian motion $B$ and its …
3
votes
Accepted
Condition for $f^\prime$ to be absolute integrable
A sufficient condition is unimodality of $f$, namely the existence of some $c \in~]a,b[$, such that $f$ non-decreasing on $[a,c]$ and $f$ non-increasing on $[a,c]$. If this property holds, then by Fat …
2
votes
Accepted
Conditional probability distribution of a Brownian particle surviving forever
By the law of Large numbers, $X_t/t \to b$ almost surely as $t \to +\infty$, hence $X_t \to +\infty$ almost surely as $t \to +\infty$. Therefore $X_\infty = +\infty$ almost surely under $P$ and also u …
2
votes
Density of $W_t$ assuming it stayed above a line $L$
As I mentionned in my comment, there is an ambiguity in the statement of you question.
Anyway, if $B$ is a standard one dimensional Brownian motion, if $\lambda$ is a real number, then $(B_t-\lambda t …
2
votes
Conditional expectation of linear combination of Rademacher RVs
I give a crude lower bound, which does not use the distribution of $Z$, but only that $|Z|=\sqrt{d}$. It relies on the triangle inequality for the angular distance on the unit sphere.
I assume $c_1$ a …
2
votes
Accepted
Correlation for a Sum of random vectors from the sphere multiplied by matrices
Is $Y$ independent of $(X_1,\ldots,X_n)$? If yes, write it.
If yes, it suffices to prove that for any fixed unit vector $u$, $\langle u,Y \rangle \le c/\sqrt{d}$ with high probability. By rotational i …
1
vote
Accepted
Can an a.s. non constant continuous martingale be differentiable with nonzero probability?
Almost surely, we can write for every $t \ge 0$, $M_t=M_0+\beta_{\langle M,M \rangle_t}$, where $\beta$ is some Brownian motion.
By Kahane's theorem, almost surely, for every $s \ge 0$, $\limsup_{\del …
1
vote
Is there something like a "self-avoiding Markov chain" on a continuous space?
In dimension 2, we have the Schramm and Loewner evolutions, very nice processes which are invariant by conformal maps (up to time-changes).
https://en.wikipedia.org/wiki/Schramm%E2%80%93Loewner_evolut …
1
vote
Precise asymptotics for moments of order statistics of normal distribution
I call $f$ the density and $F$ the cumulative distribution function of $\mathcal{N}(0,1)$.
Since $X_{(n)} \ge X_{(n-1)}$,
$$X_{(n)}-X_{(n-1)} = \int_\mathbb{R} 1_{[X_{(n-1)} \le x < X_{(n)}]}dx.$$
Tak …