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Show $\sigma_{X}^{2}(t)=\begin{cases} x_{0}\frac{\beta}{\alpha}e^{\alpha t}[e^{\alpha t}-1], & \alpha \neq 0\\ x_{0}\beta t, & \alpha = 0 \end{cases}$

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Probability every hyperplane contains at most $m/2$ vectors

Characteristic Function and Random Variable Transformation

Conditional Expectation involving Uniform Random Variables. [duplicate]

Expected tetrahedron volume from normal distribution

Density of randomly packing a box

Probability measures on a Polish space

Conditional Probability and Division by Zero

Find the probability mass function of $Y=\min(X,c)$

Pove that $Y=μ+σX$ if $X\sim N(0,1)$.

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Show $\sigma_{X}^{2}(t)=\begin{cases} x_{0}\frac{\beta}{\alpha}e^{\alpha t}[e^{\alpha t}-1], & \alpha \neq 0\\ x_{0}\beta t, & \alpha = 0 \end{cases}$

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

Bayes probability excercise

Finding the Expected value

Probability Question relating prison break

Elevator stops in building

Probability of global epidemic

Convex functions in integral inequality

Probability every hyperplane contains at most $m/2$ vectors

Characteristic Function and Random Variable Transformation

Conditional Expectation involving Uniform Random Variables. [duplicate]

Expected tetrahedron volume from normal distribution

Density of randomly packing a box

Probability measures on a Polish space

Conditional Probability and Division by Zero

Find the probability mass function of $Y=\min(X,c)$

Pove that $Y=μ+σX$ if $X\sim N(0,1)$.