How can we find its hessian, or even the gradient? how do we take its derivative in every coordinate? ]]>

in your example its not true that 9 >= 10 + 10 - 1.

see, if you rearrange you get:

Ti + Tj <= 1 + Tij

so there is an upper bound.

]]>In the begin of the semester we said that:

p(x) = 1/Z * sum ij∈E (φij (xi, xj ))

And in the approximate inference we said that:

p(x) = 1/Z * (sum ij∈E (φij (xi, xj ) + sum i∈E (φi (xi))

what is the correct definition? Witch one of them should be used in Q4?

If the second is correct, why we add the singleton part?

ido

]]>i don't understand why the conditions bounds the solution.

for teta_i, teta_j, i can choose any positive integer i like - for example 10 and 10, and for teta_ij i can choose 9.

it seems this assignment stands in all the conditions, and it seems we can choose any greater positive integer and it we will still ger good assignment.

If there is not bound then there is no solution that can max f(teta), right?

]]>How is transforming the teta matrix into a matrix that has zeros in every cell but the bottom right doesn't cause data loss on the distribution? Since we consider only p(1,1) for every ij in E when maximizing.

Thank!

]]>In our case - it is $n$, right?

Thanks

]]>Its not clear from the question.

Thanks!

]]>Also does the lambda in subsection c refer to the second one (-min(1 - tau_i)) otherwise it can't make the function greater. ]]>

I did not understand something about the importance sampling.

At the question it is written that Eq[Z]=Ep[f(X)] but f was not used at the side of Eq[Z].

At class we used f that is an indicator function. Is that what meant here also? ]]>