Bayesian Inference - Discrete Distributions
Let Z be some random variable. Then associated with Z is a probability distribution function that assigns probabilities to the different outcomes Z can take.
If Z is discrete, then its distribution is called a probability mass function, which measures the probability Z takes on the value k: P(Z=k). The probability mass function completely describes Z.
Poisson distribution
Z is Poisson-distributed if:
1P(Z=k) = \frac{\lambda^k e^{-\lambda}}{k!}, k=1,2,3,...
A Z with distribution Poisson is denoted as
Its expected value is equal to its parameter:
References
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