👉 Likelihood engineering is a methodology used in machine learning and artificial intelligence to optimize model performance by defining and maximizing the likelihood of observing the training data given a particular model. It involves formulating the problem in terms of probabilistic models, where the goal is to find model parameters that best explain the data distribution. This approach often employs techniques like Bayesian inference, variational inference, or gradient-based optimization to adjust parameters in a way that increases the probability of the model's predictions matching the observed data. By focusing on maximizing likelihood, likelihood engineering helps in building robust models that generalize well to unseen data, reducing overfitting and improving predictive accuracy.
