Use this tag only if your question is about the modern theoretical footing for probability, for example probability spaces, random variables, law of large numbers, and central limit theorems. Use [tag:probability] instead for specific problems and explicit computations. Use ...

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Estimate on Probability of a standard normal variable

In the book written by Karatzas & Shreve, at the page - 111; the authors have mentioned about a result: If $Z_{v}$ be a standard normal variable; then for $\epsilon \gt 0$ ; $\mathbb P ...
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1answer
19 views

Production line probability problem

A manufacturer has six distinct motors in stock, two of which came from a particular supplier. The motors must be divided among two production lines, with three motors going to each line. If ...
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1answer
13 views

Convergence of normal random variables

Let $(\mu_n)_{n \geq 1}$ be a sequence of real numbers and $(\sigma_n)_{n \geq 1}$ be a sequence of positive numbers, and let $\mu \in \mathbb{R}$ and $\sigma>0$. For $n \geq 1$, let ...
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3answers
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Joint probability uniform distribution

I have a question on finding probabilities of joint distributions, specifically two independent random variables that are Uniformly distributed. The question I wish to solve is this one: We agree ...
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3answers
31 views

Largest possible value of $P(A \cap B)$

Suppose $A$ and $B$ are events with $P(A)+P(B)>1$. Show that the largest possible value of $P(A \cap B)$ is $ \min(P(A), P(B))$. I suspect I'm supposed to use $P(A \cap B) = P(A)+P(B) -P(A ...
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1answer
12 views

Pairwise independence implies independence for classes on susbsets of a probability space?

Let $(\mathcal{C}_i)_{i\in I}$ be pairwise independent classes of the probability space $(\Omega, \mathcal{F},P)$, i.e. $\mathcal{C}_i,~\mathcal{C}_j$ are independent for every $i\neq j$ in $I$. True ...
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0answers
12 views

Reduction of Two Independent Random Variables in Quadratic Form

Consider the $n \times 1$ random vector $\mathbf{x}$ and the $p \times 1$ random vector $\mathbf{y}$. The vectors are independent of each other, and $\mathbf{y}$ has an expected value of zero. I want ...
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1answer
21 views

Convergence of product of random variables with distribution $U(0, e)$

Let $X_1, X_2, \ldots$ be a sequence of independent random variables with uniform distribution on $[0, e]$. Let $R_n:=\prod_{k=1}^n X_k$. By Kolmogorov's zero–one law $(R_n)$ converges with ...
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19 views

Functional Equation of Probability Distributions

Suppose you have a real random variable $X$ that has probability distribution $f_X$ meaning $$ P(\alpha \le X \le \beta) = \int_{\alpha}^{\beta} f_X(x) \ dx $$ Now assume $\Phi(f_X)$ is also a ...
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1answer
14 views

Convergence of sequences of random variable

Let $X$ be a random variable. Show that $\frac{X}{n}$ converges to zero in probability and almost surely, as $n \rightarrow \infty$. I am sort of confused by this question since I only learnt a ...
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0answers
12 views

Convexity for Hoeffding's Inequality

We consider a r.v. $X$ that satisfies $0 \leq X \leq 1$ a.s. and a sample of $n$ i.i.d. random variables $X_1,\dots, X_n$ with the same distribution as $X$. We denote by $\mu= E[X]$ and we let ...
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2answers
32 views

$f$ has a zero integral on every measurable set. Prove $f$ is zero almost everywhere

I am trying to solve the following exercise: Let $f$ be integrable. Assume that $\int_A f d\mu = 0$ for every measurable set $A$. Prove that $f = 0$ a.e. [$\mu$]. I have the following proof but it ...
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2answers
234 views

Infinite expectation implies infinite random variable?

Let $X$ be a nonnegative integer-valued random variable depending on a parameter $L$. Assume that $E[X] \rightarrow \infty$ as $L\rightarrow \infty$. Does this imply that for any $k$, $P(X > k)$ ...
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0answers
23 views

Sampling efficiently conditioned on linear constraints modulo both $\mathbb{F}_p$ and $\mathbb{F}_2$.

Given a prime $p$ and positive integer $t \ll \log p$ (say $t = \sqrt{\log p}$), is there an algorithm that is polynomial time in $\log p$ to sample uniform $X, Y \in \mathbb{F}_p$ conditioned on the ...
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0answers
22 views

Return time for two independent one dimensional random walks

Let $X^1$ and $X^{-1}$ be two simple random walk in $\mathbb{Z}$ starting respectively from $1$ and $-1$. Let $\tau$ be the first time one of them reaches the origin, $$\tau = \inf \{ j \geq 0 \, : ...
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1answer
31 views

Finding $E[X^{2}]$ of a random variable

I am having a little confusion with finding $E[X^{2}]$ that perhaps can be cleared up relatively easily. Here, $A, B, C$ are Poisson random variables with parameters $2.6, 3,$ and $3.4$, respectively. ...
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14 views

“Mean-field results” in Probability theory

I'm studying a paper on (biological) Neural Networks, and the paper studies some stability properties of an $N$-sized network, and then, as $N$ tends to infinity, it is proven that a "mean-field ...
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11 views

mutual information and data processing inequality for $X\to Y\to Z$ where $Y=f(X)$

Let $X\to Y\to Z$ be three random variables. The data processing inequality states $I(X;Y)\geq I(X;Z)$. Further assume $Y=f(X)$ where $f:\mathcal{X}\to\mathcal{Y}$ is an arbitrary function. What ...
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1answer
124 views

How do I show that the probability of the union of events is not larger than the sum of the individual probabilities?

In numeric analysis class, we are supposed to show that $$P\Bigl(\bigcup_{n\in\mathbb N}A_n\Bigr)\le\sum_{n\in\mathbb N}P(A_n).$$ This is easy to show using induction for a union of finitely many ...
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27 views

Optimal strategy for a moment to take an exam

In my problem set there was an exercise involving optimal stopping theory. Here is the problem: There is an exam, a list of $n$ questions and $n$ students. Student $A$ knows answers to $k$ of them. He ...
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2answers
26 views

Convergence of Sum of Random variable to another - Cantor function

Let $(X_{n})_{n\geq1}$ be i.i.d. Ber$\left(\frac{1}{2}\right)$. I want to show that $$\sum_{{n\geq1}}\frac{2X_{n}}{3^{n}}$$ converges almost surely to a random variable $X$, without saying that this ...
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0answers
11 views

Sufficient unconditional moment condition for the convergence of $\sum_n (X_n - E[X_n])$

Let $F_n$ be a filtration and $X_n$ be $F_n$ measurable. Then $M_n = \sum_{k=1}^{n} (X_k -E[X_k])$ is a $F_n$ measurable martingale. Lets assume that it is a square integrable one. Then one can put ...
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0answers
16 views

Preserving independence of random variables

Suppose I have three random variables, $X,Y,Z$ with $X$ independent of $Z$, $Y$ independent of $Z$. Which transformation can I apply to $X,Y$ to that the result is again a random variable independent ...
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20 views

SLLN when the expectation in infinite

In a Post I found it says: Whenever ${\rm E}(X)$ exists (finite or infinite), the strong law of large numbers holds. That is, if $X_1,X_2,\ldots$ is a sequence of i.i.d. random variables with ...
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1answer
11 views

Absolute expectation of stopped martingale

Let $M_0,M_1,\dots$ be a martingale with respect to $X_0,X_1,\dots$ and $T$ be a stopping time with respect to $X_0,X_1,\dots$ Define $T_n=\min\{n,T\}$ and let $M_{T_n}$ be the stopped martingale. By ...
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1answer
39 views

Probability theory with the hyperreals?

Forgive the undoubted ignorance of this question. I am out of my element both in probability and in nonstandard analysis. A mathematically curious layperson friend recently had a conversation with me ...
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1answer
44 views

If $\sum\limits_n \sqrt{\mathrm{Var}(X_n)}$ converges, then $\sum\limits_n (X_n - E[X_n])$ converges in $L^2$

I have seen in a paper to claim the following: If $\sum\limits_n \sqrt{\mathrm{Var}(X_n)} < \infty$, then $\sum\limits_n (X_n - E[X_n])$ converges in $L^2$, for any sequence of random ...
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1answer
18 views

Indicator function and liminf and limsup

Can anyone please explain why the following is true? And what is the intuition behind it? $$\chi_A(x) = \begin{cases}1 &, x \in A\\ 0 &, x \notin A.\end{cases}$$ Then we have ...
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1answer
36 views

Determining an upper bound

I have a function $$f(\lambda)=n\ln(1-p+pe^{\frac{\lambda}{n}})-\lambda p$$ I need to prove that $$f(\lambda)\leq \frac{\lambda^2}{8n}$$ using Taylor expansion. I have used the taylor expansion for ...
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13 views

Convergence of stochastic processes via convergence of infinitesimal generators

Given a sequence of sequence processes $(X_N(\cdot))_{N \geq 0}$, I want to show this sequence converges to another process $X(\cdot)$ by considering that the sequence of generators $(A_N)_{N \geq 0}$ ...
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1answer
38 views

A counterexample for supremum of stopping times

Let $\mathbb{F} = \{ \mathcal{F}_t \}_{t \geq 0}$ be a continuous time filteration. $\tau : \Omega \to [0, \infty]$ is called an $\mathbb{F}$-stopping time if $\{ \tau \leq t \} \in \mathcal{F}_t$ for ...
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0answers
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Expectation of Conditional expectation over incorrect distribution

We want to compute the following quantity \begin{align} \int E[V|W=u] f_U(u) du \end{align} For random variables $ V,W,U$ where $V$ and $U$ are independent and $U$ is absolutely continuous r.v. Is ...
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2answers
54 views

Product of i.i.d. random variables uniformly distributed on $(-1,1)$ converges almost surely to $0$

Let $(\Omega,\mathcal{F},P)$ be a probability space and $(X_n)$ i.i.d. random variables with uniform distribution on $(-1,1)$. If $Y_n=X_1\ldots X_n$, prove that $(Y_n)$ converges to $0$ a.s.
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1answer
37 views

Lebesgue integral of vector-valued function?

In Bernt Øksendals stochastic differential equations he says that if we have a random variable $X:\Omega\rightarrow\mathbb{R}^d$. He defines the expectation: $E[X]=\int_\Omega ...
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1answer
23 views

Congruent measurable sets

I have a question regarding Congruent relations: In Euclidean geometry, two subsets of $\mathbb{R}^{d}$ are said to be congruent if one set can be mapped onto the other by translations and rotations. ...
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0answers
41 views

If the sample space is an Euclidean Space, we can use a different type of PDF

The title resume all the point I'll try to make now. Reading this post, I realize that is possible to have another type of PDF (probability density function). Usually, we have a probability space ...
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1answer
21 views

Martingale Transform counterexample

I am studying discrete time martingale theory and came across the classical "You can't beat the system" theorem: given a martingale $M$ and a previsible process $C=(C_n)_{ n \ge 1}$ such that $C_n$ is ...
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11 views

Modelling the ballot theorem as a martingale.

The page 19 in the link http://www.imada.sdu.dk/~jbj/DM839/FL15.pdf provides the explanation of what a ballot theorem is and how we can prove that it is a martingale. It takes a random variable $S_k$ ...
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1answer
37 views

Weak law of large numbers works although first moment

I want to prove that the weak-law of large numbers hold if $X_{k}$ has the distribution: $$\mathbb{P}\left(X_k\leq x\right) = \int_{-\infty}^x c\left(1+t^2\right)^{-1}\left(\log(1+t^2)\right)^{-1}dt ...
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1answer
25 views

examples of random variables that the result of their preimage is not in F?

let's assume we have a probability space $(\Omega , F , P)$. and we have a random variable $X$ defined as : $X : \Omega \rightarrow \Bbb{R}$ and we also use a Borel set ($\mathcal{B}$).(making the ...
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1answer
26 views

How do I transform this Probability in weak law of large number form?

Let $X_{1},\dots$ be a sequence of independent random variables. Suppose, for $k=1,2,\dots$ $$P\left(X_{2k-1}=1\right)=P\left(X_{2k-1}=-1\right)=\frac{1}{2}$$ and the probability density function of ...
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2answers
29 views

What probability mass, density or distribution function might corresponds to this moment generating function? [duplicate]

I have somehow come up with a random variable $X$ with moment generating function (assuming it exists) $$M_{X}(t) = -t (1 - e^t)$$ What is the probability mass, density or distribution function? It ...
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1answer
27 views

Weak convergence of measures and compact sets

Suppose that we have a sequence of probability measures $\{ \mathbb{P}_n \}$ converging weakly to a probability measure $\mathbb{P}$. Suppose that $M$ is a metric space with a compact subset $K$. I ...
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1answer
44 views

How to show a sequence of independent random variables do not almost surely converge by definition?

I have a sequence of independent random variables $X_1, X_2, \ldots$ where $$ X_n = \begin{cases} 1 & \quad \text{with probability} \ 1/n \\ 0 & \quad \text{with ...
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1answer
25 views

How to show convergence in probability by just using the definition?

I have a series of random variables $X_1, X_2, \ldots$ where $$ f(X_n) = \begin{cases} 1/n & \quad \text{if} \ X_n = 1 \\ 1-1/n & \quad \text{if} \ X_n = 0 \\ 0 & ...
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1answer
46 views

What is the intuitive difference between almost sure convergence and convergence in probability? [duplicate]

It is a standard fact in probability that almost sure convergence is stronger than convergence in probability. I can only see the differences in the proof. However, is there a way to view it ...
3
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1answer
19 views

Moment generating function and convergent random variables

denote by $X$ and $X_n$, $n\in \mathbb{N}$, random variables and $r\in\mathbb{R}_+$ with $E=\mathbb{E}\left[ e^{rX} \right] < \infty$ and $E_n=\mathbb{E}\left[ e^{rX_n} \right] < \infty$ for all ...
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0answers
26 views

Dealing with Recurrence Relations of Random Variables

Let $(Y_n)_{n\in \mathbb N} $ be some sequence of independent random variables, and $(X_n)_{n\in \mathbb N} $ another sequence, defined recursively as follows: $$X_{n+1} = \alpha X_n + \beta Y_n ...
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0answers
27 views

Probability to get from A to C.

There has been a snowstorm and Bob is trying to drive from A to C. p and q are the probabilities that the two roads are passable. What is the probability that Bob can get from A to C? Note that ...
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1answer
21 views

Stopping time and the Martingale stopping theorem.

According to the book that I am reading, A nonnegative, integer valued random variable T is a stopping time for the sequence {$Z_{n},n\geqslant0$} if the event T = n depends only on the value of ...