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An event defines a complementary event, namely the complementary set (the event not occurring), and together these define a Bernoulli trial: did the event occur or not? Typically, when the sample space is finite, any subset of the sample space is an event (that is, all elements of the power set of the sample space are defined as events). [5]
The meaning in this article corresponds to what is used in e.g. Bayesian hierarchical modeling. The special case for compound probability distributions where the parametrized distribution is the Poisson distribution is also called mixed Poisson distribution.
Independence is a fundamental notion in probability theory, as in statistics and the theory of stochastic processes.Two events are independent, statistically independent, or stochastically independent [1] if, informally speaking, the occurrence of one does not affect the probability of occurrence of the other or, equivalently, does not affect the odds.
A probability is a way of assigning every event a value between zero and one, with the requirement that the event made up of all possible results (in our example, the event {1,2,3,4,5,6}) is assigned a value of one. To qualify as a probability, the assignment of values must satisfy the requirement that for any collection of mutually exclusive ...
A compound Poisson distribution, in which the summands have an exponential distribution, was used by Revfeim to model the distribution of the total rainfall in a day, where each day contains a Poisson-distributed number of events each of which provides an amount of rainfall which has an exponential distribution. [13]
In medical genetics, compound heterozygosity is the condition of having two or more heterogeneous recessive alleles at a particular locus that can cause genetic disease in a heterozygous state; that is, an organism is a compound heterozygote when it has two recessive alleles for the same gene, but with those two alleles being different from each other (for example, both alleles might be ...
Generally, there is only one event B such that A and B are both mutually exclusive and exhaustive; that event is the complement of A. The complement of an event A is usually denoted as A′, A c, A or A. Given an event, the event and its complementary event define a Bernoulli trial: did the event occur or not?
In this situation, the event A can be analyzed by a conditional probability with respect to B. If the event of interest is A and the event B is known or assumed to have occurred, "the conditional probability of A given B", or "the probability of A under the condition B", is usually written as P(A|B) [2] or occasionally P B (A).