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Summary
| Description |
English: Bayes rule for Gaussians. For the prior p(x) (blue) m0=20, λ0=1 and the likelihood p(y∣x) (red) mD=25 and λD=3 , the posterior p(x∣y) (magenta) shows the posterior distribution with m=23.75 and λ=4 . The posterior is closer to the likelihood than the prior because the likelihood has higher precision. Bayes rule for Gaussians has been used to explain many behaviours from sensory integration to collective decision making. |
| Date | |
| Source | William Penny. "Bayesian Models of Brain and Behaviour", ISRN Biomathematics doi:10.5402/2012/785791 |
| Author | William Penny |
| Permission (Reusing this file) |
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
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| Date/Time | Thumbnail | Dimensions | User | Comment | |
|---|---|---|---|---|---|
| current | 16:13, 14 February 2020 | | 600 × 488 (44 KB) | JoramSoch | Uploaded by the NOA Upload Tool |
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