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Expected Value of MLE estimate over standard deviation and expected deviation

A slecture by ECE student Zhenpeng Zhao

Partly based on the ECE662 Spring 2014 lecture material of Prof. Mireille Boutin.




1. Motivation

  • Most likely converge as number of number of training sample increase.
  • Simpler than alternate methods such as Bayesian technique.



2. Motivation

  • Statistical Density Theory Context
    • Given c classes + some knowledge about features $ x \in \mathbb{R}^n $ (or some other space)
    • Given training data, $ x_j\sim\rho(x)=\sum\limits_{i=1}^n\rho(x|w_i) Prob(w_i) $, unknown class $ w_{ij} $ for $ x_j $ is know, $ \forall{j}=1,...,N $ (N hopefully large enough)
    • In order to make decision, we need to estimate

$ \rho(x|w_i) $, $ Prob(w_i) $ $ \rightarrow $ use Bayes rule,

or $ \rho(x|w_i) $ $ \rightarrow $ use Neyman-Pearson Criterion


Zhenpeng Selecture 1.png Zhenpeng Selecture 2.png Zhenpeng Selecture 3.png Zhenpeng Selecture 4.png Zhenpeng Selecture 5.png



(create a question page and put a link below)

Questions and comments

If you have any questions, comments, etc. please post them on https://kiwi.ecn.purdue.edu/rhea/index.php/ECE662Selecture_ZHenpengMLE_Ques.


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