If the events are not mutually exclusive then
$ \mathrm{P}\left(A \hbox{ or } B\right)=\mathrm{P}\left(A\right)+\mathrm{P}\left(B\right)-\mathrm{P}\left(A \mbox{ and } B\right) $
Conditional probability is written P(A|B), and is read "the probability of A, given B"
$ P(A \mid B) = \frac{P(A \cap B)}{P(B)}\, $
Discrete-time Fourier Transform Pairs and Properties | |
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Property of Probability Functions | |
The complement of an event A (i.e. the event A not occurring) | $ \,P(A^c) = 1 - P(A)\, $ |
The intersection of two independent events A and B | $ \,P(A \mbox{ and }B) = P(A \cap B) = P(A) P(B)\, $ |
The union of two events A and B (i.e. either A or B occurring) | $ \,P(A \mbox{ or } B) = P(A) + P(B) - P(A \mbox{ and } B)\, $ |
The union of two mutually exclusive events A and B | $ \,P(A \mbox{ or } B) = P(A \cup B)= P(A) + P(B)\, $ |
DT Fourier Transform Pairs | |||
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$ x[n] $ | $ \longrightarrow $ | $ \mathcal{X}(\omega) $ | |
DTFT of a complex exponential | $ e^{jw_0n} $ | $ \pi\sum_{l=-\infty}^{+\infty}\delta(w-w_0-2\pi l) \ $ | |
$ a^{n} u[n], |a|<1 \ $ | $ \frac{1}{1-ae^{-j\omega}} \ $ | ||
$ \sin\left(\omega _0 n\right) u[n] \ $ | $ \frac{1}{2j}\left( \frac{1}{1-e^{-j(\omega -\omega _0)}}-\frac{1}{1-e^{-j(\omega +\omega _0)}}\right) $ |
DT Fourier Transform Properties | |||
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$ x[n] $ | $ \longrightarrow $ | $ \mathcal{X}(\omega) $ | |
multiplication property | $ x[n]y[n] \ $ | $ \frac{1}{2\pi} \int_{2\pi} X(\theta)Y(\omega-\theta)d\theta $ | |
convolution property | $ x[n]*y[n] \! $ | $ X(\omega)Y(\omega) \! $ | |
time reversal | $ \ x[-n] $ | $ \ X(-\omega) $ |
Other DT Fourier Transform Properties | |
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Parseval's relation | $ \frac {1}{N} \sum_{n=-\infty}^{\infty}\left| x[n] \right|^2 = $ |