Timeline for How to structure this problem in a bayesian paradigm? (Updating the posterior?)
Current License: CC BY-SA 4.0
9 events
| when toggle format | what | by | license | comment | |
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| Nov 3, 2020 at 15:32 | comment | added | Michael Tamillow | I am a little confused by what you mean by "neighbors". I think your "neighbor" comment is just covariance. Neighbors would require some dimensionality and distance measure. Furthermore covariance requires some value. A "smooth curve" doesn't mean very much. Your problem seems misspecified. If you want to do updates on these 91-positions, you might use a dirichlet distribution. And I would say drop the "smooth curve" idea altogether unless you can specify a full joint probability distribution. | |
| S May 14, 2018 at 22:18 | history | bounty ended | O.rka | ||
| S May 14, 2018 at 22:18 | history | notice removed | O.rka | ||
| May 14, 2018 at 22:18 | vote | accept | O.rka | ||
| May 11, 2018 at 17:28 | answer | added | user1993951 | timeline score: 2 | |
| May 8, 2018 at 18:54 | comment | added | jbowman | Your problem as stated in the text looks like an Approximate Dynamic Programming - type problem, where you're trying to come up with a function that approximates the reward function, but in your final question you treat it as a statistical updating problem. I don't see that there's any randomness other than your choice of initial random number. Why not just calculate the function over the range of values 10...100, then you know exactly what it is? | |
| S May 7, 2018 at 22:53 | history | bounty started | O.rka | ||
| S May 7, 2018 at 22:53 | history | notice added | O.rka | Draw attention | |
| May 5, 2018 at 22:07 | history | asked | O.rka | CC BY-SA 4.0 |