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 Jul4 comment Sum of two kronecker products as a kronecker product Thank you. Matrices are square symmetric and pos. def., and of same size (A and B's). But I can't directly say anything about their dependence. Jul4 comment Sum of two kronecker products as a kronecker product thanks! Actually I have $(A_1\otimes B_1) + (A_2 \otimes B_2)$ and want to come up with such a representation. Your answer starts with this, so if there is such a representation, then they are linearly dependent (right?). But if we don't know but want to come up with such a representation, I think we can not assume linear dependence. Is that right? so what are the conditions of existence? Jul4 revised Sum of two kronecker products as a kronecker product added 109 characters in body Jul4 asked Sum of two kronecker products as a kronecker product Apr24 revised Convergence in probability of iid normal random variables deleted 4 characters in body Mar26 comment MCMC/E-M limitations?MCMC over E-M? MCMC does not necessarily faster than EM. EM gives you a point estimate while MCMC would give you the full posterior. For your question: As long as you derive a valid MCMC algorithm for the models you mentioned (for example if you can derive a Gibbs sampler, or a valid MH sampler), then you don't have to prove any convergence. The notion of "convergence" is not well-defined in MCMC by the way... Feb8 awarded Popular Question Jan24 comment Which is the difference between $P(A \mid B)$ and $P(A=t \mid B)$ in a Bayesian Network? If $A$ and $B$ are both random variables, to reason about $P(A=t | B)$, you have to fix $B = b$. For example $P(A | B=b)$ could represent a probability mass function, and $P(A=t|B=b)$ is probability that $A$ being $t$ conditioned on a specific value of $B$. Jan18 awarded Teacher Jan16 answered What does it mean u(dx) in the Fourier transform of a probability measure u? Jan2 comment Fourier Transform of Constant Function Dirichlet conditions (including integrability) are sufficient, not necessary. Dec31 awarded Popular Question Dec12 awarded Caucus Nov27 awarded Popular Question Nov17 comment Finite state Markov chain A finite state (discrete-time) Markov chain is a sequence of random variables $X_1,X_2,\ldots$ all of which take values from a finite set $\mathcal{X}$. Nov7 awarded Yearling Oct23 awarded Popular Question Oct15 comment Maximum likelihood estimate of $N$ (trials) in Binomial For fixed $x$ ($x=4$), we can write $p(x=4 | \pi,N) = \mathcal{L}(N,\pi)$. So $\pi^*$ is a maximiser for a fixed $N$, I still can not see that why this is true. Shouldn't we show that $N$ is unique in some way? May be if I plot $\mathcal{L}$ wrt $(N,\pi)$, that would shed some light. Oct15 revised Maximum likelihood estimate of $N$ (trials) in Binomial edited body Oct14 asked Maximum likelihood estimate of $N$ (trials) in Binomial