Parameter Estimation


## Model Fitting
### finds the most likely parameter $\theta_*$ that explain the data
if $\Theta \subset R$, $Risk = bias^2 + variance$
statistical experiment be a sample $X_1$  … , $X_n$ of i.i.d.random variables in some measurable space Ω, usually Ω ⊆ ℝ
hyperparameter $\alpha$, $D$ is data set
확률 계산시 조건은 데이터이고
결과 파라미터는 데이터값으로 인한 식으로 나와야한다
### Parameter Estimations
|Title|
|:-:|
|[MLE](https://texonom.com/mle-79884be98a0447628b39ab3953780b4a)|
|[MAP](https://texonom.com/map-9ecf5e2396fb4901983a57761a3e7174)|
|[EM Algorithm](https://texonom.com/em-algorithm-1c9ae15421d24b00afc033e7ccc743a9)|
|[Bayesian inference](https://texonom.com/bayesian-inference-e3cabe7844954db097867a732143ddd8)|
|[Back Propagation](https://texonom.com/back-propagation-18f4493692ad43449d4271f1bb293781)|





> [Variational Inference 알아보기 - MLE, MAP부터 ELBO까지](https://modulabs.co.kr/blog/variational-inference-intro/)






