If the event \(\mathrm{B}\) occurs does not change the probability that event \(\mathrm{A}\) occurs, and event \(\mathrm{A}\) and \(\mathrm{B}\) are the independent event then
\(\Rightarrow \mathrm{P}(\mathrm{A} \mid \mathrm{B})=\mathrm{P}(\mathrm{A})\)
According to bayes's theorem It states the relation between the probabilities of \(\mathrm{A}\) and \(\mathrm{B,~ P(A)}\) and \(\mathrm{P(B)}\) and the conditional probabilities of \(\mathrm{A}\) gives \(\mathrm{B}\) and \(\mathrm{B}\) gives \(\mathrm{A}\).
\(\Rightarrow \mathrm{P}(\mathrm{A} \mid \mathrm{B})\) and \(\mathrm{P}(\mathrm{B} \mid \mathrm{A})\)
\(\Rightarrow \mathrm{P(A} \mid \mathrm{B})=\frac{\mathrm{P(B} \mid \mathrm{A}) \cdot \mathrm{P(A)}}{\mathrm{P(B)}}\)
\(\Rightarrow \mathrm{P}(\mathrm{B} \mid \mathrm{A})=\frac{\mathrm{P}(\mathrm{A} \cap \mathrm{B})}{\mathrm{P}(\mathrm{A})}\)
\(\Rightarrow \mathrm{P}(\mathrm{A} \cap \mathrm{B})=\mathrm{P}(\mathrm{A}) \cdot \mathrm{P}(\mathrm{B})\)
\(\Rightarrow \mathrm{P}(\mathrm{A} \mid \mathrm{B})=\frac{\mathrm{P}(\mathrm{A} \cap \mathrm{B}) \cdot \mathrm{P}(\mathrm{A})}{\mathrm{P}(\mathrm{A}) \cdot \mathrm{P}(\mathrm{B})}\)
\(\Rightarrow \mathrm{P}(\mathrm{A} \mid \mathrm{B})=\frac{\mathrm{P}(\mathrm{A}) \cdot \mathrm{P}(\mathrm{B}) \cdot \mathrm{P}(\mathrm{A})}{ \mathrm{P}(\mathrm{A}) \cdot \mathrm{P}(\mathrm{B})}\)
\(\Rightarrow \mathrm{P}(\mathrm{A} \mid \mathrm{B})=\mathrm{P}(\mathrm{A})\)
\(\therefore\) The value of \(\mathrm{P(A} \mid \mathrm{B})\) is \(\mathrm{P(A)}\).