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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Is the product of two Gaussian random variables also a Gaussian?

Say I have $X \sim \mathcal N(a, b)$ and $Y\sim \mathcal N(c, d)$. Is $XY$ also normally distributed? Is the answer any different if we know that $X$ and $Y$ are independent?
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What is the difference and relationship between the binomial and Bernoulli distributions?

How should I understand the difference or relationship between binomial and Bernoulli distribution?
user122358
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Colliding Bullets

I saw this problem yesterday on reddit and I can't come up with a reasonable way to work it out. Once per second, a bullet is fired starting from $x=0$ with a uniformly random speed in $[0,1]$. If two bullets collide, they both disappear. If we…
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What does it mean to integrate with respect to the distribution function?

If $f(x)$ is a density function and $F(x)$ is a distribution function of a random variable $X$ then I understand that the expectation of x is often written as: $$E(X) = \int x f(x) dx$$ where the bounds of integration are implicitly $-\infty$ and…
Jeromy Anglim
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Generate Correlated Normal Random Variables

I know that for the $2$-dimensional case: given a correlation $\rho$ you can generate the first and second values, $ X_1 $ and $X_2$, from the standard normal distribution. Then from there make $X_3$ a linear combination of the two $X_3 = \rho X_1 +…
jameselmore
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Affine transformation applied to a multivariate Gaussian random variable - what is the mean vector and covariance matrix of the new variable?

Given a random vector $\mathbf x \sim N(\mathbf{\bar x}, \mathbf{C_x})$ with normal distribution. $\mathbf{\bar x}$ is the mean value vector and $\mathbf{C_x}$ is the covariance matrix of $\mathbf{x}$. An affine transformation is applied to the…
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Computing the Expectation of the Square of a Random Variable: $ \text{E}[X^{2}] $.

What is the rule for computing $ \text{E}[X^{2}] $, where $ \text{E} $ is the expectation operator and $ X $ is a random variable? Let $ S $ be a sample space, and let $ p(x) $ denote the probability mass function of $ X $. Is $$ \text{E}[X^{2}] =…
CodeKingPlusPlus
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Sum of two independent binomial variables

How can I formally prove that the sum of two independent binomial variables X and Y with same parameter p is also a binomial ?
Piyush Maheshwari
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Cutting out a circle using circles

Let $X_0$ be the unit disc, and consider the process of "cutting out circles", where to construct $X_n$ you select a uniform random point $x \in X_{n-1}$, and cut out the largest circle with center $x$. To illustrate this process, we have the…
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"Random" generation of rotation matrices

For a current project, I need to generate several $3\times 3$ rotation matrices for input into an algorithm. I thought I might go about this by randomly generating the number of elements needed to define a rotation matrix and then calculating the…
bob.sacamento
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How to compute the sum of random variables of geometric distribution

Let $X_{i}$, $i=1,2,\dots, n$, be independent random variables of geometric distribution, that is, $P(X_{i}=m)=p(1-p)^{m-1}$. How to compute the PDF of their sum $\sum_{i=1}^{n}X_{i}$? I know intuitively it's a negative binomial distribution…
TonyLic
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Sum of squares of dependent Gaussian random variables

Ok, so the Chi-Squared distribution with $n$ degrees of freedom is the sum of the squares of $n$ independent Gaussian random variables. The trouble is, my Gaussian random variables are not independent. They do however all have zero mean and the same…
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Mean and variance of Squared Gaussian: $Y=X^2$ where: $X\sim\mathcal{N}(0,\sigma^2)$?

What is the mean and variance of Squared Gaussian: $Y=X^2$ where: $X\sim\mathcal{N}(0,\sigma^2)$? It is interesting to note that Gaussian R.V here is zero-mean and non-central Chi-square Distribution doesn't work. Thanks.
kaka
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Why is the roll of a die considered random?

I've been reading articles on pseudo-randomness in computing when generating a random value. They all state that the generated numbers are pseudo-random because we know all the factors that influence the outcome, and that the roll of a die is…
Max Koretskyi
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Convergence in probability of the product of two random variables

Suppose $\{X_n\}$ and $\{Y_n\}$ converge in probability to $X$ and $Y$, respectively. Will $X_n Y_n$ converge in probability to $X Y$? I know the answer is yes. If we treat $(X_n,Y_n)$ as a random vector, and it converges in probability to $(X,Y)$…
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