👉 Likelihood computing is a fundamental concept in probabilistic machine learning and statistical inference, focusing on estimating the probability of a given data set or observation given a model. It involves calculating the likelihood, which is the probability of observing the data under the assumption that a particular model parameter set is true. This is often done using Bayes' theorem, which combines the prior probability of the model parameters with the likelihood of the data to produce a posterior probability. In practice, likelihood computing is crucial for model training and evaluation, as it helps determine how well a model fits the data and guides decisions on model selection and hyperparameter tuning. The approach is widely used in various applications, from natural language processing to computer vision, where understanding the probability of data given a model is essential for making accurate predictions and decisions.
