Questions tagged [random-variables]

Questions about maps from a probability space to a measure space which are measurable.

A random variable $X: \Omega \to E$ is a measurable function from a set of possible outcomes $\Omega$ to a measurable space $E$. The technical axiomatic definition requires $\Omega$ to be a sample space of a probability triple. Usually $X$ is real-valued.

The probability that $X$ takes on a value in a measurable set $S \subseteq E$ is written as :

$$P(X \in S) = P(\{ \omega \in \Omega|X(\omega) \in S\})$$

where $P$ is the probability measure equipped with $\Omega$.

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General Addition Rule for Variances (Perfect Positive Correlation)

Confused on the general addition rule for variances. Why is it that when two random variables, X and Y, have a perfect positive correlation (p=1) their standard deviations add. But when they are uncorrelated (p=0) their variances add?
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geometric distribution and expected value

How I can resolve this problem ? Set $X \sim \operatorname{Geo}(p)$, $0 \leqslant p \leqslant 1$. Evaluate $\operatorname{E}(e^{-X})$. I can't use $e^{-X}$ in this distribution.
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Confused about notation of max of random variables

Let (N, F) be a down-closed set system. $\forall$ e $\in$ N, there is a non-negative random variable $Z_e$. I'm confused about what the following notation means: $I^* \in argmax \{\, \sum_{e \in I} Z_e \mid I \in F \,\}$ I'm a little confused about…
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How to use Random Variables

I'm reading Introduction to Algorithms-Cormen, Leiserson, Rivest, Stein, Section 9.2 Selection in Expected linear time. Page-217 They have defined Indicator Random Variable $X_k$ as $X_k$ = I {the subarray A[p..q] has exactly k elements} and…
Atinesh
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probability and expectation without replacement

From a box containing N identical tickets numbered 1 to N, n tickets are drawn without replacement. Let X be the largest number drawn. Find E[X] I got the pdf as x-1Cn-1/NCn And E(X)=summation x*pdf Here i am stuck and i dont know whether what i…
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Show two random variables have the same distribution?

I need help with this question. Thanks :-)! : Assume that X is uniform on $[0, 1]$ and that $F$ is the cdf of a continuous random variable Y . Show that $Z = F^{−1}(X)$ has the same distribution as $Y$ . (Note: $X$ uniform on $[a, b]$ means that…
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Can the range of a variable be inclusive infinity?

Can a range be $[0, \infty]$ or must it be $[0, \infty)$ because you can never quite reach infinity? Clarification: $[0, 1]$ means $0 \leqslant x \leqslant 1 $, while $(0, 1)$ means $0 < x < 1 $. My question is whether infinity can be written as…
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Does normalization of a random vector, destroy uniformity?

If I have a random vector in Rn that has a uniform distribution in the domain [a,b]n, a<0, b>0. Is uniformity lost or preserved (in the unit sphere) if I normalize the vector (using the euclidean norm)?
tinyhippo
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Help with the limits of integrals in a function of two random variables

I have this problem, I say that $x=z-y$, hence $F_z(z)=\iint\limits_D f(x,y)\ dx\ dy$, now I think that the limits for the $x$ integral would be from $0$ to $z-y$ and for $y$ would be from $0$ to $1$. I am not sure if I can say that I need to…
Peterson
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Computing a complicated variance

In a lottery $n$ numbers are selected from the $N$ numbers $1,2,\cdots,N.$ Find the variance of the sum $S_n$ of the selected numbers. My idea: We want to find $P(S_n=k)$. Now, it would be the number of solutions of $$x_1+x_2+\dots+x_n=k$$…
shadow10
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Geometric Random Variable

I'm reading about Geometric Random variables from a book, which is as follows: $X_1, X_2,\ldots$ are independent identically distributed variables which are $\mathrm{Ber}(p)$ $$ Y = \min \{n\geq 1\mid X_n = 1\} \sim \mathrm{Geo}(p)$$ $$ Y \in\mathbb…
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Poisson Random Variable

Let us model the number of winter storms in a given year as a Poisson random variable. Suppose that in a good year the average number of storms is 3, and in a bad year the average is 5. If the next year will be good with probability 40% and bad with…
Jilly
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Let $X$ be a continuous random variable, uniformly distributed on the interval $(0, 1)$.

Let $X$ be a continuous random variable, uniformly distributed on the interval $(0, 1)$. Let $Y = \sqrt{X}$ and $Z = 1/X$. Show that: 1. For $0 < a < 1$, $\mathrm{P}(Y
user2850514
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Two random variables - prove or disprove

I need to prove or disprove the following: There exist two random variables $X,Y$ s.t: $Var[X]=Var[Y]=2$ $Cov(X,Y)=4$ I tried to stick to the definitions here and couldn't find any…
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Random Variable with non-decreasing function - Inclusion

Let $X$ be a random variable and $f$ a non-decreasing function on the range of $X$. If $Y=f(X)$, then $$\{X\le q\}\subset\{Y\le f(q)\} \quad\quad\text{and}\quad\quad \{X
Phil-ZXX
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