FYI Likelihood refers to a function of parameters given the observed data.
L(θ)=P(x∣θ).
Likelihood being larger supports a particular choice of parameter estimate, ergo one may write some hypothesis is likely (in response to the observation of one or more events).
The likelihood of a hypothesis is distinct from the probability of a hypothesis under both bayesianism and frequentism.
Likelihood is not a probability: it does not integrate to unity over the parameter space, and scaling it up to a monotonic transformation does not change its usage or meaning.
I digress, the main point is there is no such thing as the likelihood of an event. Again, Likelihood is a function of the parameter viz. the hypothesis. Every hypothesis has a likelihood (and a probability, presuming you are a bayesian). Every event has a probability, but not a likelihood.
FYI Likelihood refers to a function of parameters given the observed data.
L(θ)=P(x∣θ).
Likelihood being larger supports a particular choice of parameter estimate, ergo one may write some hypothesis is likely (in response to the observation of one or more events).
The likelihood of a hypothesis is distinct from the probability of a hypothesis under both bayesianism and frequentism.
Likelihood is not a probability: it does not integrate to unity over the parameter space, and scaling it up to a monotonic transformation does not change its usage or meaning.
I digress, the main point is there is no such thing as the likelihood of an event. Again, Likelihood is a function of the parameter viz. the hypothesis. Every hypothesis has a likelihood (and a probability, presuming you are a bayesian). Every event has a probability, but not a likelihood.