Probability Density Function (PDF) Intuition
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Can a probability distribution value exceeding 1 be OK?
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Given an exact formula, how can Mathematica find a probability distribution whose PDF matches it?
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$X$ and $Y$ are independent rv having pdf $f(t)=\frac{1}{\pi} \frac{1}{1+t^2}$ determine pdf of $Z=\frac{X+Y}{3}$
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Distribution function terminology (PDF, CDF, PMF, etc.)
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P.d.f of the absolute value of a normally distributed variable
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What is the intuitive sense behind the purpose and mechanics of Sufficient Statistics?
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Bayes' Theorem Intuition
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Find values for probability density function
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Different probability density transformations due to Jacobian factor
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PDF and CDF of sum of random variables with different distributions
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Constructing inversion method from a given pdf by finding inverse of cdf
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If $X$ has a $U[-1, 3]$ distribution and $Y = X^2$, find the probability density function of $Y$ .
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Intuition behind using complementary CDF to compute expectation for nonnegative random variables
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How to find the mode of a probability density function?
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From distribution function to probability measure
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What is the link (discrepancy?) between these PDF/CDF and p-value distributions?
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What is the reason that a likelihood function is not a pdf?