Recent content by sensitive

I am still having trouble solving P(S = N|F = N). please help...

Hi I am doing this exercise (2 class problem with 2-dimensional features) and I have solved the linear discriminant function which turns out be y1(x) - y2(x) = 2x1 +2x2 I am having difficulty in finding the class posterior probabilities frm the linear discriminant function obtained...

So the Bayesian approach was right. I taught I was wrong at the first place because using Bayesian ended up with the following P(S = N|F = N) P(F = N)/ P(S = N) but from the diagram I have and as you can see from the probabilities, S depend on both F and T and in the expression above we...

I am having problem solving this exercise. The problem actually comes with a diagram but I do not know and I do not think i can draw it in the forum. The exercise is based on car starting(Heckerman 1995) Since I can't draw the network diagram here but values of probability are given but...

Well I should have said this earlier. The time series is a random Gaussian noise so Zt is independent and identically distributed. hence E[Zt] = 0 --> which is the expectation(mean) and E[(Zt)^2] = σ^2 --> expectation variance. To answer your question the expectation of each term in the...

Is the following ARMA (2,1) model stationary? xt + 1/6xt-1 – 1/3xt-2 = εt + 0.7εt-1 Inorder to know if a model is stationary. we check the mean, variance and the covariance and check whether it is dependent on time. Obviously the mean is zero but my problem is how do i carry out the...

I am reading a topic on Bayesian Inference.I read books from different authors but they are all the same. I cannot see how the terms are derived. Could anyone briefly explain what is going on and what is it that we are trying to find using this Bayesian. Bayesian is a combination of belief...

Thx you very much..I got the ans. The formula really helps.. Thx for any inputs..:)

Regarding the previous question, am I on the right track? Thx. Whoops sori i didnt know there was a next page. Well briefly we have been taught on that Bayes theorem. But I should say the formula you gave is not familiar to me or I might have come across under maximum liklihood. I will...

whoops let my jus correct a typo; p(innocent) = 1 - p(guilty) = 1 - (1/10000)

probability that someone is either innocent or guilty can be written as we know P(guilty) = 1/10000 so p(innocent) = 1 - (1/100000) hence p(guilty or innocent) = p(guilty) + p(innocent) probability that there is a DNA match and the person is either innocent or guilty can be written as...

Yes i agree that a person is innocent iff he is not guilty. but there is still a probability of a match although a person is innocent. therefore shouldn't that be considered in the p(DNA match). Correct me if I am wrong. Thx..

I overlooked your reply. Yes I meant to say p(guilty) = 1/10000... sori abt that..

well my understanding is that there are two possibilities that there is a DNA match if a person is guilty and there is also a probability that a person is innocent. The statement probability that aperson is innocent has nothing to do with the p(guilty) but associated to the p(DNA match)

I am trying my best to get my head around this ques. This is what i came up with. p(DNA match) = p(DNA match|Guilty)*p(DNA match|innocent) p(Guilty|DNA match) = (p(DNA match|Guilty) /10000)/(p(DNA match) I hope I am making sense somewhere.. thx