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2015-10-02
Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference - de Cameron Davidson-Pilon Davidson-Pilon (Author)
Caractéristiques Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference
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| Le Titre Du Fichier | Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference |
| Date de Parution | 2015-10-02 |
| Traducteur | Pinchos Naithan |
| Chiffre de Pages | 562 Pages |
| La taille du fichier | 64.88 MB |
| Langue du Livre | Français et Anglais |
| Éditeur | Roaring Brook Press |
| ISBN-10 | 2711809847-MUK |
| Format de Document | EPub PDF AMZ DOCX WOLF |
| de (Auteur) | Cameron Davidson-Pilon Davidson-Pilon |
| Digital ISBN | 421-7889922149-XDR |
| Nom de Fichier | Bayesian-Methods-for-Hackers-Probabilistic-Programming-and-Bayesian-Inference.pdf |
Télécharger Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference Livre PDF Gratuit
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 DavidsonPilon introduces Bayesian inference from a computational perspective bridging theory to practice–freeing you to get results using computing power
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Bayesian Methods for Hackers Probabilistic Programming and Bayesian Inference 2015
Bayesian Methods for Hackers Probabilistic Programming and Bayesian Inference AddisonWesley Data Analytics Series 1st Edition Kindle Edition
Bayesian Methods for Hackers illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy SciPy and Matplotlib Using this approach you can reach effective solutions in small increments without extensive mathematical intervention DavidsonPilon begins by introducing the concepts underlying Bayesian inference
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Caractéristiques techniques du livre Bayesian methods for hackers probabilistic programming and bayesian methods