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What is MLE vs Bayesian Estimation Explained about?

- 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