Master Bayesian Inference through Practical Examples and Computation - Without Advanced Mathematical Analysis.
Bayesian methods of inference are deeply natural and extremely powerful. However, most discussions of Bayesian inference rely on intensely complex mathematical analyses and artificial examples, making it inaccessible to anyone without a strong mathematical background. Now, though, Cameron Davidson-Pilon introduces Bayesian inference from a computational perspective, bridging theory to practice–freeing you to get results using computing power.
| Pages : | 256 pages |
| Size : | PDF Files |
| Downloads: | 117 |
| Created: | 2020-08-28 |
| License: | MIT License |
| Author(s): | Cameron Davidson-Pilon |
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