[Proof] MSE = Variance + Bias²

[@matheusmaldaner]

The first explanation I found that takes the time to expand Bias^2. Thank you!

[@renzo1745]

Love your demonstrations

[@mathetal]

Thank u 💛

[@sammymendis475]

Thank you so much! I spent too much time trying to figure this out and this was so clear.

[@HaykTarkhanyan]

You're doing a great job, thank you.

[@HuaXiao-c1m]

amazing

[@swaggy745]

if we are given a pdf of 4 values of x with their probabilities in terms of theta, then we find an estimator for the mean theta-hat and then we find the mean square error in terms of theta (should it be in terms of theta?), how can we find if it it mean square consistent. I am unsure because n=4 for my questions so I can't see how it makes sense to consider the limit as n goes to infinity. Please could someone shed some light. Thank you

[@sophia17965]

2:23 why not distributing expected value E to theta^2???

[@sophia17965]

i dont understand the distributive properties of expectation

[@cindyli2186]

@@sophia17965since theta squared is a constant, the expected value of a constant is simply the constant

[@adityabaghel1270]

Yay!! Awesome

[@MimirYgg]

thank you