Timeline for Paramaeter estimation in noisy conditions with Machine Learning, possible?
Current License: CC BY-SA 4.0
9 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Dec 12, 2018 at 12:10 | comment | added | ignatius | Sorry for that, it was my fault not to mention that $\theta$ is a vector... | |
| Dec 12, 2018 at 12:03 | comment | added | Romain Reboulleau | It's because I thought theta was just a scalar value! With a multi-dimensional output it is much more complicated. I'll think about it. | |
| Dec 12, 2018 at 9:24 | comment | added | ignatius | I have added the mathematical formulation of the problem. I think that trying to get a ML estimator analytically will be quite difficult since all the observed values $\theta$ are not independent, they come from a sensing unit which is a black-box for us.... so trying to make assumptions about its pdf will be very challenging. For your "data-driven" approach, Iam sorry but i don't understand the idea of sorting the samples... | |
| Dec 11, 2018 at 19:38 | history | edited | Romain Reboulleau | CC BY-SA 4.0 | further explanation of the machine learning way, minor corrections in the statistical approach |
| Dec 11, 2018 at 17:12 | comment | added | ignatius | OK, I'll take my time to read your second approach. I think I can also give more details about the model and the data. Thank you so much! | |
| Dec 11, 2018 at 17:08 | comment | added | Romain Reboulleau | OK, I think I get the idea now. Check out my second proposition in the edit. If I have more time I'll try to explain the idea further, later today or tomorrow. | |
| Dec 11, 2018 at 17:08 | history | edited | Romain Reboulleau | CC BY-SA 4.0 | added 1219 characters in body |
| Dec 11, 2018 at 16:42 | comment | added | ignatius | Thank you for your response. What you mention is the statistical approach, which I have worked with. I have worked with ML estimation, CRLB, fisher information matrices and so on... so, I suggested the same to my colleague: try to characterize the noise and go for ML estimation, or reconsider if the model does not fit your data. I was asking for another approaches... if they exist.. | |
| Dec 11, 2018 at 16:37 | history | answered | Romain Reboulleau | CC BY-SA 4.0 |