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Bayesian methods of inference are deeply natural and extremely powerful. Cameron Davidson-Pilon introduces Bayesian inference from a computational perspective, bridging theory to practice, freeing you to get results using computing power. 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.
Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference
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HNL 1895
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Bayesian methods of inference are deeply natural and extremely powerful. Cameron Davidson-Pilon introduces Bayesian inference from a computational perspective, bridging theory to practice, freeing you to get results using computing power. 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.
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Detalles de producto
| Publisher | Addison-Wesley Professional |
| Publication date | October 2, 2015 |
| Edition | 1st |
| Language | English |
| Print length | 256 pages |
| ISBN-10 | 0133902838 |
| ISBN-13 | 978-0133902839 |
| Item Weight | 13.5 ounces (382.73 grams) |
| Dimensions | 7 x 0.57 x 9.13 inches (17.8 x 1.4 x 23.2 cm) |
Who Should Buy?
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Data Scientists
Data scientists looking to deepen their understanding of Bayesian methods and probabilistic programming will find invaluable insights.
-
Statisticians
Statisticians interested in applying Bayesian inference techniques in their analyses and projects can greatly benefit from this book.
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Software Engineers
Software engineers wanting to implement statistical models using probabilistic programming languages will gain practical skills from this resource.
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Complete Beginners
Individuals with no background in statistics or programming may struggle to grasp the advanced concepts presented in this book.
DESCRIPCIÓN DEL PRODUCTO
Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference
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Data Mining Editorial Review
"Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference" is a comprehensive learning guide to probabilistic programming and Bayesian inference presented through Python libraries such as PyMC. The book has a fast-paced narrative with heavy coding, but it is well worth the effort it takes to follow. The book, in general, received positive reviews, especially from beginners starting with PyMC. However, readers complained about the low quality of the Addison Wesley paper edition, with all the colored figures printed in black and white, and the font size being too small. Some readers found the book less useful than they had hoped as the book does a poor job of showing the mathematical formalism of the examples/topics and connecting them to the actual code. Some readers also struggled to understand the examples as the author assumes readers know about all the function and method calls it correlates with. Moreover, most of the matplotlib formatting code could have been handled with a stylesheet, thereby removing it from the meaningful code. Despite these issues, some readers love the jupyter notebooks that accompany the book, and some volunteers have converted them to the latest version. Overall, the book is highly recommended as a starting point for learning probabilistic programming.
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ventajas
- A comprehensive guide to probabilistic programming and Bayesian inference.
- The book is paced quickly and has a lot of content.
- The jupyter notebooks that accompany the book.
- An excellent starting point for someone learning PyMC or Bayesian statistics.
Contras
- The Low quality of the Addison Wesley paper edition.
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características y beneficios
- Deeply natural and extremely powerful Bayesian methods of inference
- Learn Bayesian inference from a computational perspective using PyMC
- Illuminates Bayesian inference through probabilistic programming with NumPy, SciPy, and Matplotlib
- Building and debugging models, testing model's “goodness of fit”
- Overcoming exploration versus exploitation dilemma, using Bayesian Inference to improve A/B testing
- Solving data science problems with small amounts of data
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