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Summary
| Description |
English: In statistics, Hamiltonian Monte Carlo is a technique commonly used to reconstruct unobserved parameters based on observed data. Bayes theorem is used to calculate the posterior probability of every possible set of parameters, and a physics simulation is used to sample that posterior probability distribution.
The two axes of the plot represent two coupled parameters. The shading and contours represent the posterior probability distribution, where white is lower and green is higher. Starting from an arbitrary guess, the simulation stochastically travels to a variety of likely points, which are all accepted as plausible answers. |
| Date | |
| Source | Own work using: github.com/jkunimune/hamiltonian-mc |
| Author | Justinkunimune |
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Licensing
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This file is made available under the Creative Commons CC0 1.0 Universal Public Domain Dedication. |
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| Date/Time | Thumbnail | Dimensions | User | Comment | |
|---|---|---|---|---|---|
| current | 00:01, 25 March 2024 | | 548 × 284 (9.29 MB) | Justinkunimune | Uploaded own work with UploadWizard |
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