An observation 'r' is characterised under binary hypotheses according to the following condi- tional probability density...

 

Transcribed Image Text:

An observation 'r' is characterised under binary hypotheses according to the following condi- tional probability density functions Ho: fr(r|Ho)= e #, r >= 0 TAO H: fr(r|H1)= r >= 0 r <0 (1) (2) respectively. Find the MAP (5+3+2=10M) The probability of the two hypotheses Ho and H are and rule and ML rule for optimal decision. Also comment on the result. An observation 'r' is characterised under binary hypotheses according to the following condi- tional probability density functions Ho: fr(r|Ho)= e #, r >= 0 TAO H: fr(r|H1)= r >= 0 r <0 (1) (2) respectively. Find the MAP (5+3+2=10M) The probability of the two hypotheses Ho and H are and rule and ML rule for optimal decision. Also comment on the result.

Answer rating: 100% (QA)

To find the Maximum A Posteriori MAP rule and Maximum Likelihood ML rule for optimal decisionmaking ... View the full answer