MLE
[KL Divergence](https://texonom.com/kl-divergence-c7964872f5184a7baaf312605405aef6) [Cost Function](https://texonom.com/cost-function-66c21423cc7347909016b3423f2ada7e) 


## Maximum likelihood estimation
MLE는 MAP의 Prior가 Uniform Distribution인 경우
We can also minimize $-logL(\theta)$
### Maximum likelihood estimation
|Title|
|:-:|
|[Likelihood function](https://texonom.com/likelihood-function-0750726af35a45bb86036dbc7507f725)|
|[Log-likelihood function](https://texonom.com/log-likelihood-function-c824711ae9454e53a4347add60060762)|
|[Complete Log Likelihood](https://texonom.com/complete-log-likelihood-30cc920673524e68838bd08c831b8090)|


### Maximum likelihood estimatiors
[Stochastic Gradient Descent](https://texonom.com/stochastic-gradient-descent-d8b8d008e0a34f4bb55175ffba21db44) (Ascent)
[Gradient Descent](https://texonom.com/gradient-descent-c1342b13182f4fb6959023a75b5e2ff8) 
[Newton–Raphson method](https://texonom.com/newtonraphson-method-0bc249bfb0fe4e498829110bf785eb6c) 


> [1. Linear Basis Function Models](http://norman3.github.io/prml/docs/chapter03/1)

> [MLE와 MAP의 차이](https://niceguy1575.medium.com/mle와-map의-차이-7d2cc0bee9c)





