Statistical inference
Likelihood-based inference

Likelihood-based inference is a paradigm used to estimate the parameters of a statistical model based on observed data. Likelihoodism approaches statistics by using the likelihood function, denoted as L (θ | x) {\displaystyle L(\theta |x)}, quantifies the probability of observing the given data x {\displaystyle x}, assuming a specific set of parameter values θ {\displaystyle \theta }. In likelihood-based inference, the goal is to find the set of parameter values that maximizes the likelihood function, or equivalently, maximizes the probability of observing the given data.

The process of likelihood-based inference usually involves the following steps:
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