User raskol - MathOverflowmost recent 30 from http://mathoverflow.net2013-05-19T18:03:53Zhttp://mathoverflow.net/feeds/user/29611http://www.creativecommons.org/licenses/by-nc/2.5/rdfhttp://mathoverflow.net/questions/124686/equivalent-markov-random-fieldsEquivalent Markov Random Fieldsraskol2013-03-16T11:42:44Z2013-05-10T09:22:00Z
<p>Hi,</p>
<p>Is it possible to have topologically different Markov Random Fields (few different edges) and yet yielding the same inference results ?</p>
<p>Thanks!</p>
http://mathoverflow.net/questions/125884/kl-divergences-comparisonKL divergence(s) comparison,raskol2013-03-29T07:39:45Z2013-03-29T22:31:00Z
<p>Hi,</p>
<p>$P_1$, $P_2$, $P_3$ are probability distributions defined on the same support.</p>
<p>Knowing that $H(P_1) < H(P_2) < H(P_3)$, can we compare $D_{KL}(P_2,P_1)$ and $D_{KL}(P_3,P_1)$ ?</p>
<p>(H is the Shannon Entropy and $D_{KL}$ is the Kullback–Leibler divergence)</p>
<p>Thank you.</p>
http://mathoverflow.net/questions/121159/equilibrium-of-random-zero-sum-gameEquilibrium of random zero-sum game,raskol2013-02-08T07:27:07Z2013-02-08T08:45:54Z
<p>Hi,</p>
<p>How to find, or at least express, the equilibrium of a zero-sum game with an $n*n$ payoff matrix (each player has $n$ strategies) and the payoff of the entry $(i,j)$ is $u(i,j)$. $u$ a random function of the strategies $i$ and $j$.</p>
<p>What about the case where $n \rightarrow \infty$.</p>
<p>Any reference or code is welcome.</p>
<p>Thanks a lot!</p>
http://mathoverflow.net/questions/115270/how-to-work-with-infinite-random-graphsHow to work with infinite random graph(s) ?raskol2012-12-03T10:54:28Z2012-12-03T14:50:14Z
<p>Hi,</p>
<p>In the case where we are dealing with an infinite random graph (RG with infinite nodes).</p>
<p>How do we model/work with notions like degrees, degree distribution ? How are they defined ?</p>
<p>Thanks!</p>
http://mathoverflow.net/questions/115258/softmax-activation-function-with-infinite-supportsoftmax activation function with infinite support ?raskol2012-12-03T07:39:43Z2012-12-03T07:39:43Z
<p>Hi,</p>
<p>How do we calculate the terms of a <a href="http://en.wikipedia.org/wiki/Softmax_activation_function" rel="nofollow">softmax activation function</a> with an infinite support ?</p>
<p>That is, finding the $\{p_i\}_i$ with <a href="http://en.wikipedia.org/wiki/Softmax_activation_function" rel="nofollow">$p_i = {{e^{q_i}} \over {\sum_{j=1}^\infty e^{q_j}
}}$</a> (how to estimate the infinite sum?)</p>
<p>Any proposition or idea is welcome.</p>
<p>Thanks a lot!</p>
http://mathoverflow.net/questions/125884/kl-divergences-comparison/125948#125948Comment by raskolraskol2013-04-01T15:54:12Z2013-04-01T15:54:12ZThank you.
If we specify that KL is continuous at $(S_2, S_1)$ (respectively $(S_3, S_1)$) and that the distributions $S_1$, $S_2$, $S_3$ are strictly positive over all the support elements.
Is it possible to characterize $D_{KL}(P_2,P_1)/D_{KL}(P_3,P_1)$ ?http://mathoverflow.net/questions/125884/kl-divergences-comparison/125948#125948Comment by raskolraskol2013-04-01T15:52:31Z2013-04-01T15:52:31ZThank you.
If we specify that KL is continuous at $(S_2, S_1)$ (respectively $(S_3, S_1)) and that the distributions $S_1$, $S_2$, $S_3$ are strictly positive over all the support elements, is it possible to characterize $D_{KL}(P_2,P_1)/D_{KL}(P_3,P_1)$ ?http://mathoverflow.net/questions/124686/equivalent-markov-random-fieldsComment by raskolraskol2013-03-18T10:32:43Z2013-03-18T10:32:43ZIn the case of Bayesian networks: "It has been noted that different Bayesian networks may be equivalent in the sense that they actually represent the same joint probability distribution (and thus conditional independency information as well), even though they have different graphical structures." (<a href="http://www.cs.uregina.ca/Research/Techreports/2002-02.ps" rel="nofollow">cs.uregina.ca/Research/Techreports/2002-02.ps</a>).
I am asking the same question for MRFs.http://mathoverflow.net/questions/115258/softmax-activation-function-with-infinite-supportComment by raskolraskol2012-12-05T07:05:36Z2012-12-05T07:05:36ZForgive me.
Supposing that we have an infinite network $G_{\infty}$ with vertices $V=[1, \infty]$.
Herein, $q_i$ is the value of the node $i \in V$.
The $q_i$ are determined by a random walk that starts from a node.