Maximum Likelihood as minimising KL Divergence

Jessica YungMachine LearningLeave a Comment

Sometimes you come across connections that are simple and beautiful. Here’s one of them! What the terms mean Maximum likelihood is a common approach to estimating parameters of a model. An example of model parameters could be the coefficients in a linear regression model , where is Gaussian noise (i.e. it’s random). Here we choose parameter values that maximise the … Read More