What is the relationship between maximum likelihood estimation and generative modeling as described in the chapter?

Maximum likelihood estimation (MLE) is a technique used in generative modeling to estimate the parameters of a model that are most likely to explain the observed data. In generative modeling, MLE involves finding the parameter values that maximize the likelihood of the data, which is equivalent to minimizing the negative log-likelihood. This approach is crucial for determining the weights of neural networks in generative models.

Maximum likelihood estimation (MLE) is a statistical method used in generative modeling to find the parameter set that maximizes the likelihood of observing the given data. In the context of generative modeling, the parameters often correspond to the weights of neural networks within the model. The goal is to adjust these parameters to maximize the likelihood function, which measures how well the model explains the observed data. This process is equivalent to minimizing the negative log-likelihood, a common practice in neural network training. However, directly calculating the likelihood is often intractable for high-dimensional problems, so different generative models employ various strategies to approximate or implicitly model the density function.