Carl Feynman
|
Registered User
|
|
|
May 21 |
comment |
Are there some potentially good methods of comparing the variances of two distribution function? This problem is underspecified. You haven't told us anything about the distributions except for the properties they <i>don't</i> have, i.e. that they are different and that they don't have analytic variances. At this point, we don't know that they have variances at all, or even means, or are even computable. What you need to do is to tell us everything you <i>do</i> know about the distributions. For example, do they have compact support? Is there some way to sample from the distributions? What space is the random variable over? |
|
May 16 |
answered | Constrained minimum maximal distance. |
|
May 9 |
comment |
bipartite graph coloring For the case where all the V1 vertices have two neighbors, this reduces to vertex-coloring an arbitrary graph, with all its NP-complete goodness. The structure you are describing as "a bipartite graph with..." is more commonly called a hypergraph. V2 is the vertices of the hypergraph and V1 is the hyperedges. |
|
May 6 |
comment |
Expected edit distance You can tighten the bound slightly. The number of sequences with the given number of insertions, deletions and substitutions is less than you give because it doen't make sense to have an insertion next to a deletion. |
|
Apr 25 |
awarded | ● Editor |
|
Apr 25 |
revised |
iteratively (approximately) solving a sum of exponentials deleted 2 characters in body |
|
Apr 25 |
answered | iteratively (approximately) solving a sum of exponentials |
|
Apr 23 |
comment |
Constraint optimization problem for any dimensionality $n>1$. Whoops, I see that my comment above is incorrect; Mr Kallus didn't give the amplitude either. |
|
Apr 23 |
comment |
Constraint optimization problem for any dimensionality $n>1$. The period is $n$. I failed to correctly specify the amplitude, but Yoav Kallus kindly supplied it for us. I was going to write up a more precise answer, but Mr Kallus beat me to it. |
|
Apr 23 |
comment |
Constraint optimization problem for any dimensionality $n>1$. That solution is a sine wave. That suggests the general solution is of the form $a_i=sin(2 \pi i / n)$. |
|
Apr 17 |
answered | In Szemerédi’s Regularity Lemma, how many blocks are in the partition? |
|
Mar 27 |
comment |
Machin-like formulas for logarithms You mention that you are looking for numbers "sandwiched between smooth numbers." What is the importance of smooth numbers for this problem? |
|
Mar 21 |
awarded | ● Yearling |
|
Mar 21 |
answered | Is there a database somewhere for sharing translations of mathematical works? (Or, is anyone interested in a translation of a letter Weil wrote to de Rham in 1946?) |
|
Feb 16 |
comment |
What machine learning algorithm is appropriate for predicting one time-series from another? I imagine that both the teacher and the student are more likely to look at interesting things then they are to look at boring things. Then areas the teacher looks at more are more likely to be looked at by the student, but not necessarily at the same time. This is impossible to capture in a Markov model because it is time-independent. On the other hand, it is space-dependent, so density estimation on the complete teacher time sequence could capture this dependence. |
|
Feb 10 |
answered | Find a convex hull that contains given points? |
|
Feb 4 |
comment |
Solving a linear recurrence relation with variable coefficients. One can produce any sequence whatever of (nonzero) vectors for $X[n]$, by choosing $f$ and $g$ correctly. And once $X$ becomes zero, it will remain zero thereafter. In the absence of any knowledge of $f$ and $g$, there is nothing more to be said. Perhaps if you explained what your actual problem is, someone might be able to help. I don't know why you're trying to keep $f$ and $g$ secret from us-- you must have some theory that constrains them. |
|
Feb 3 |
comment |
What is the cardinality of the family of unlabelled bipartite graphs on n vertices? In the limit for large $n$, the average graph has no automorphisms, so the number of unlabeled graphs is simply the number of labeled graphs divided by the number of possible labelings, i.e. $n!$. |
|
Jan 12 |
answered | equation for bowling ball on a trampoline |
|
Dec 27 |
comment |
Integration in the surreal numbers This thesis says in the conclusion that 'A “natural” definition of integral that gives the right answer for the exponential function ... is the main open problem of this thesis.' So that reference doesn't answer the question. |
|
Dec 23 |
comment |
Gradient Descent for Primal Kernel SVM with Soft-Margin(Hinge) Loss You're not going to get nice results trying to find the derivative of a function like $max$, which is not everywhere differentiable. I see two ways out. First, use a conditional in your expression, like equation (12) in your reference. Second, use a differentiable approximation to $max$, like softmax (read the last section of the Wikipedia article on softmax for the approximation I'm thinking about.) |
|
Dec 10 |
answered | Estimate number of distinct items |
|
Dec 6 |
comment |
Sampling without replacement: probability for total successes from successes in sample? This is a job for Bayes' theorem. |
|
Dec 5 |
comment |
softmax activation function with infinite support ? And once again, I ask, how are the $q_i$ determined? If you know they're produced by some stochastic process, there is some hope of being able to provide a value for their softmax. But if you don't know that process, there's no hope. |
|
Dec 4 |
awarded | ● Commentator |
|
Dec 4 |
comment |
softmax activation function with infinite support ? I'd be happy to answer this question if you clarified what you were asking. What do you know about the $q_i$? How are they determined? If you don't have any information about the $q_i$, there is no way to provide an estimate. |
|
Dec 2 |
comment |
distinguishing random orthogonal matrix from Gaussian random matrix A nonzero total variation distance doesn't necessarily mean that the distributions can be told apart from samples. The place where the distributions differ substantially might take up a vanishingly small fraction of the probability weight of the distributions. |
