Parameter Estimation

Creator
Creator
Seonglae ChoSeonglae Cho
Created
Created
2023 Mar 23 1:36
Editor
Edited
Edited
2024 Oct 29 14:32

Model Fitting, Fitting
Probability Distribution

finds the most likely parameter that explain the data

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
 
 
 
 
 
 
 

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