Conditional probability is the probability of some event A, given the occurrence of some other event B. When in a random experiment the event B is known to have occurred, the possible outcomes of the experiment are reduced to B, and hence the probability of the occurrence of A is changed from the unconditional probability into the conditional probability given B. The conditional probability fallacy is the assumption that P(A|B) is approximately equal to P(B|A).
There is also a concept of the conditional probability of an event given a discrete random variable. Such a conditional probability is a random variable in its own right.
Discrete probability distribution is a probability distribution characterized by a probability mass function. Among the most well-known discrete probability distributions that are used for statistical modeling are the Poisson Distribution, the Bernoulli distribution, the Binomial distribution, the geometric distribution, and the negative binomial distribution.
There is also a concept of the conditional probability of an event given a discrete random variable. Such a conditional probability is a random variable in its own right.Discrete probability distribution is a probability distribution characterized by a probability mass function. Among the most well-known discrete probability distributions that are used for statistical modeling are the Poisson Distribution, the Bernoulli distribution, the Binomial distribution, the geometric distribution, and the negative binomial distribution.
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