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What does the expression $Pr(x|y;\theta)$ mean? As in: $$ {\displaystyle {\begin{aligned}L(\theta |x)&=Pr(Y|X;\theta )\\&=\prod _{i}Pr(y_{i}|x_{i};\theta )\\&=\prod _{i}h_{\theta }(x_{i})^{y_{i}}(1-h_{\theta }(x_{i}))^{(1-y_{i})}\end{aligned}}} $$ https://en.wikipedia.org/wiki/Logistic_regression#Model_fitting

I've never been able to figure out what $Pr$ means. What's the difference versus $P$?

oink
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1 Answers1

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$\Pr()$ is pretty standard notation used to denote probability. In case of $\Pr(Y|X;\theta)$ it's conditional probability of $Y$ given $X$ and $ \theta$.

See also similar thread Difference between $p(x)$ vs. $\pi(x)$ in literature

Tim
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