Let x ∈ r n×p be a n × p matrix and y ∈ r n . the gaussian linear model with known variance σ 2 > 0 is defined as p = {normaln(xθ, σ2 in) | θ ∈ r p } , where normaln denotes the n-variate gaussian distribution. (a) prove that θ is identifiable if and only if x has full column rank

dont trust me im no expert these losers made me fail a test bro this sucks

step-by-step explanation:

what level of math? also i don't need to be paid if i can you.

19

step-by-step explanation:

answer: 5 17/33 % (to the tenth)

step-by-step explanation:

he guessed: 34.5 pounds

actual weight is 32.7

there for he is off:

34.5 -32.7 = 1.8

32.7 is the 100%

1.8 represents %? of 32.7

therefore:

32.7   - 100%

1.8     -     x%

x= 1.8*100%/32.7% = 18*100%/327%

x=1800%/327%   = (5 165/327)%

to the tenth:

5 17/33 %