[dumb1224]

I was doing machine learning but never dig into stats before. Then I tried to study Bayesian inference and regression by myself and finally I got what it really means and its importance. First I realised that ubiquity of 'likelihood' and 'likelihood function', then I realised it's just a way to parameterise the model parameters instead of input data. Then MLE is a way to get an estimate of maximum of that function, which is interpreted as the most likely setting to give rise to the data observed.

I know it's not statistically correct but I think it helped a lot in my understanding of other methods....

[alalv]

It is not the most likely setting to give rise to the data observed (that is the posterior), is the setting in which the data observed is the most likely.

[dumb1224]

Sorry my English in that sentence is probably flawed. I somehow still can't tell the difference very much.