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 $P$?