👉 The likelihood function, a cornerstone of probabilistic modeling, quantifies how probable a given dataset is under a specific model. Its likelihood is calculated by integrating the joint probability density function (or probability mass function for discrete cases) over all possible values of the data, effectively measuring how well the model's predictions match the observed data. A high likelihood indicates that the model is a good fit to the data, suggesting that the underlying assumptions and parameters of the model are plausible. However, it's important to note that a high likelihood doesn't necessarily imply causation or that the model is perfect; it simply reflects how well the model's predictions align with the data given its structure.
