Timeline for slwcon and eincr in integrating multivariate Gaussian random variables
Current License: CC BY-SA 3.0
12 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Apr 13, 2017 at 12:55 | history | edited | CommunityBot |
replaced http://mathematica.stackexchange.com/ with https://mathematica.stackexchange.com/
|
|
| Aug 22, 2015 at 20:29 | comment | added | Michael E2 | @Guesswhoitis. I do not doubt the claim and did not mean to insinuate there is any reason to doubt it. | |
| Aug 22, 2015 at 12:27 | comment | added | J. M.'s missing motivation♦ | Anyway: I'd believe the claim that the transcendental roots idea in its current form is based entirely on Germundsson's own research; he has been working on this for quite a while. | |
| Aug 22, 2015 at 12:14 | comment | added | J. M.'s missing motivation♦ | I think this is one point where our use of Mathematica can have an edge over the use of MATLAB by Chebfun. Since the powerful idea of Chebfun is the very simple idea of approximating a function with a polynomial, and Mathematica's polynomial handling seems pretty okay (Gröbner methods require symbolic manipulation, and MATLAB doesn't have that), in principle it should be possible to do stuff that is difficult for Chebfun. I'm dreaming of so many possibilities right now… :D | |
| Aug 22, 2015 at 12:07 | comment | added | Michael E2 |
Thanks, @ciao. It's a fairly simple, if advanced, technique and seems quite powerful. I hope people will appreciate Guess's other answers. My first thought was, "I wonder if this is what NSolve does?" But WRI claims the breakthrough in NSolve was due to their own research..
|
|
| Aug 22, 2015 at 12:01 | comment | added | Michael E2 | @Guesswhoitis. Thanks for introducing it to the site. (I had missed your earliest answer, somehow.) Students arrived on campus yesterday and I was too busy to do any real work on it. Boyd's simplified overview makes choosing $n$ seem simple, but I didn't have time to look into it. | |
| Aug 22, 2015 at 7:18 | history | edited | J. M.'s missing motivation♦ | CC BY-SA 3.0 |
deleted 2 characters in body
|
| Aug 22, 2015 at 6:33 | comment | added | ciao | +1 for link to most interesting answer... | |
| Aug 22, 2015 at 5:59 | comment | added | J. M.'s missing motivation♦ |
Nice to see someone excited about this like I am. :) I've seen the paper on the bivariate Chebyshev approximant; there's still a lot to digest before I can try implementing it myself (unless you beat me to it ;) ). BTW: NIntegrate[] is capable of (tensor product) Clenshaw-Curtis quadrature, so at least that one is able to avoid an ad hoc choice of n like mine.
|
|
| Aug 22, 2015 at 3:04 | history | edited | Michael E2 | CC BY-SA 3.0 |
Added alternate solution
|
| Aug 22, 2015 at 2:53 | comment | added | user32416 |
Thanks. For $g \equiv 1$, this is indeed a great approach (I suspect the same as symbolic integration). But for general $g$, it seems like NExpectation collapses back to NIntegrate anyway.
|
|
| Aug 22, 2015 at 1:25 | history | answered | Michael E2 | CC BY-SA 3.0 |