I was studying parameter estimation from Sheldon Ross' probability and statistics book. Here the task of parameter estimation is described as follows:
Is this task the same of density estimation in machine learning contexts?
Mathematics for Machine Learning book describes density estimation as follows:
My question arises from the fact that a probability density function is fully described by its parameters (e.g. a Gaussian or normal distribution is a density function which is fully described by its mean $\mu$ and its variance $\sigma^2$).