About this document
MLE vs Bayesian Estimation Explained by Nikhil Gupta is a document available to read on EtoBox.
- The document discusses maximum likelihood estimation and Bayesian parameter estimation for pattern recognition applications. - It describes the challenges of not having complete probabilistic knowledge and instead having training data. The goal is to use training data to estimate unknown parameters of the classifier. - Maximum likelihood estimation finds the parameter values that maximize the probability of obtaining the observed training samples. Bayesian estimation treats parameters as random variab
- Author
- Nikhil Gupta
- Language
- EN