×
Well done. You've clicked the tower. This would actually achieve something if you had logged in first. Use the key for that. The name takes you home. This is where all the applicables sit. And you can't apply any changes to my site unless you are logged in.

Our policy is best summarized as "we don't care about _you_, we care about _them_", no emails, so no forgetting your password. You have no rights. It's like you don't even exist. If you publish material, I reserve the right to remove it, or use it myself.

Don't impersonate. Don't name someone involuntarily. You can lose everything if you cross the line, and no, I won't cancel your automatic payments first, so you'll have to do it the hard way. See how serious this sounds? That's how serious you're meant to take these.

×
Register


Required. 150 characters or fewer. Letters, digits and @/./+/-/_ only.
  • Your password can’t be too similar to your other personal information.
  • Your password must contain at least 8 characters.
  • Your password can’t be a commonly used password.
  • Your password can’t be entirely numeric.
Enter the same password as before, for verification.
Login

Grow A Dic
Define A Word
Make Space
Set Task
Mark Post
Apply Votestyle
Create Votes
(From: saved spaces)
Exclude Votes
Apply Dic
Exclude Dic

Drift estimation in stochastic flows using kernel integral operators

Click here to flash read.

arXiv:2404.10698v2 Announce Type: replace
Abstract: A stochastic differential equation (SDE) describes a motion in which a particle is governed simultaneously by the direction provided by a vector field / drift, and the scattering effects of white noise. The resulting motion can only be described as a random process instead of a solution curve. Due to the non-deterministic nature of this motion, the task of determining the drift from data is quite challenging, since the data does not directly represent the directional information of the flow. This paper describes an interpretation of vector field as a conditional expectation, which makes its estimation feasible via kernel-integral methods. It presents a numerical procedure based on kernel integral operators, that computes this expectation. In addition, some techniques are presented which can overcome the challenge of dimensionality if the SDE's carry some structure enabling sparsity.

Click here to read this post out
ID: 813400; Unique Viewers: 0
Unique Voters: 0
Total Votes: 0
Votes:
Latest Change: April 19, 2024, 7:32 a.m. Changes:
/u/anonymous
Dictionaries:
Words:
Spaces:
Views: 12
CC:
No creative common's license
Comments: