Seonglae ChoMaximum likelihood estimation
MLE is the case where MAP's Prior is a Uniform Distribution (i.e., not considering prior probability)
We usually assume that training data is iid hence
For computational reasons, we work with the NLL with minimizing it
Therefore we search with Log-likelihood function
NLL (Negative log likelihood)
For example with Bernoulli Distribution
Expanding the probability
Grouping terms for
where ,
MLE is given by which is the empirical fraction of heads
MLE Notion
1. Linear Basis Function Models
http://norman3.github.io/prml/docs/chapter03/1
MLE와 MAP의 차이
데이터 분석에서 모델을 다루게 되면 (암묵적으로) 분포를 가정합니다.
https://niceguy1575.medium.com/mle와-map의-차이-7d2cc0bee9c
머신러닝 - 최대우도추정 이해하기.
최대우도추정에 대하여 아래와 같은 내용을 이해할 수 있습니다.
#최대우도추정 #최대우도
기부하기: https://toon.at/donate/637413907797802062
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