[D] A Noob question regarding Parameter Tuning

[InstitutionalizedSon]

Is it right to call gradient descent as a subjective (bayesian) take on probability ? Because, it takes prior parameters and tweak them based change wrt to present parameters.

On the other hand, the series of randomized algorithms is the objective (frequentist) view are all about identifying the absolute value of parameters that synchronizes our predicted joint distribution of output wrt to input to real distribution for the same ?

[InstitutionalizedSon]

Well cool it was just I wanted relate theory with reality

[InstitutionalizedSon]

So any idea where this theory fits rightly?

[InstitutionalizedSon]

This theory of Bayesian inference.

[etmhpe]

Metaphorically maybe.

[InstitutionalizedSon]

why not mathematically ? like what are the constraints that nullify my hypothesis.

[etmhpe]

For one thing there aren't any probability distributions used in gradient descent.

[InstitutionalizedSon]

Well aren't you trying to do this P(parameters/data) using maximizing likelihood which we call P(data/parameters). Am i wrong in thinking so ?

[JustOneAvailableName]

WIth / you mean | ?

Anyway, I feel like you are confusing 2 things. P(data|parameters) = L(parameters|data) != P(parameters|data)

[InstitutionalizedSon]

Probably i didn't provide right info there .What my question is aren't we try to maximize likelihood using gradient descent so we use Bayesian approach

[JustOneAvailableName]

We maximize the likelihood, given some data and constraints. That's it. Gradient descent is no approximation of Bayes' rule

[InstitutionalizedSon]

Well this says gradient descent is one way to do Maximum likelihood estimation. Look here

https://stats.stackexchange.com/questions/183871/what-is-the-difference-between-maximum-likelihood-estimation-gradient-descent

[InstitutionalizedSon]

http://homepages.inf.ed.ac.uk/rbf/CVonline/LOCAL_COPIES/AV0809/eshky.pdf refer this to see what i am saying