Parameter Estimation

Creator
Creator
Seonglae ChoSeonglae Cho
Created
Created
2023 Mar 23 1:36
Editor
Edited
Edited
2024 Nov 19 12:12

Model Fitting, Fitting
Probability Distribution
, Parametric Learning

Methods find the most likely parameter that explain the data and boil down to
if ,
statistical experiment be a sample … , of i.i.d. random variables in some measurable space Ω, usually Ω ⊆ ℝ
hyperparameter , is data set
  • While performing MLE estimation, we update the weights through back propagation to maximize the likelihood of the data, obtaining the optimal point estimation
  • While performing MAP estimation, we update the weights through back propagation to maximize the posterior probability, obtaining the optimal point estimation
  • While performing Bayesian inference, we update the weights through back propagation to calculate the posterior probability distribution, obtaining the optimal density estimation
Point Estimations
 
 
Parameter Optimizations
 
 
 
 
 
 
 

Recommendations