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- Think Bayes: Bayesian Statistics in Python
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Nº de artículo:
3502111
Think Bayes: Bayesian Statistics in Python
90% of respondents would recommend this to a friend
Nº de artículo:
3502111
PEN 173
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With this book, you'll learn how to solve statistical problems with Python code instead of mathematical notation.
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What Stands Out
Comprehensive Coverage
Offers an extensive introduction to Bayesian statistics using Python, making it suitable for both beginners and experienced practitioners seeking to deepen their understanding.
Practical Examples
Includes numerous hands-on examples and exercises, allowing readers to apply Bayesian concepts in real-world scenarios and enhance their programming skills in Python.
Accessible Learning
Written in a clear and engaging style, the book simplifies complex topics, making Bayesian statistics approachable for those without a strong mathematical background.
Detalles de producto
- If you know how to program with Python and also know a little about probability, youâ??re ready to tackle Bayesian statistics. With this book, you'll learn how to solve statistical problems with Python code instead of mathematical notation, and use discrete probability distributions instead of continuous mathematics. Once you get the math out of the way, the Bayesian fundamentals will become clearer, and youâ??ll begin to apply these techniques to real-world problems. Bayesian statistical methods are becoming more common and more important, but not many resources are available to help beginners. Based on undergraduate classes taught by author Allen Downey, this bookâ??s computational approach helps you get a solid start. Use your existing programming skills to learn and understand Bayesian statistics Work with problems involving estimation, prediction, decision analysis, evidence, and hypothesis testing Get started with simple examples, using coins, M&Ms, Dungeons & Dragons dice, paintball, and hockey Learn computational methods for solving real-world problems, such as interpreting SAT scores, simulating kidney tumors, and modeling the human microbiome.
| Publisher | O'Reilly Media |
| Publication date | October 29, 2013 |
| Edition | 1st |
| Language | English |
| Print length | 211 pages |
| ISBN-10 | 1449370780 |
| ISBN-13 | 978-1449370787 |
| Item Weight | 12.8 ounces (362.88 grams) |
| Dimensions | 7 x 0.41 x 9.19 inches (17.8 x 1 x 23.3 cm) |
Who Should Buy?
-
Data Scientists
Ideal for data scientists looking to deepen their understanding of Bayesian statistics and implement models in Python.
-
Statistics Students
Great resource for students studying statistics, offering practical examples and hands-on coding exercises in Python.
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Machine Learning Enthusiasts
Beneficial for ML practitioners interested in incorporating Bayesian methods into their algorithms for better predictive modeling.
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Beginners in Stats
Not suitable for beginners without a foundational understanding of statistics, as it dives deeply into complex concepts.
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Probability & Statistics Editorial Review
**** "Think Bayes: Bayesian Statistics in Python" by Dr. Allen B. Downey emerges as a well-crafted introductory resource for individuals venturing into the realms of Bayesian analysis and data science. The book is praised for its clarity and effectiveness, featuring thoughtfully designed examples paired with accessible Python code. Downey’s approach integrates fundamental concepts such as probability density functions and simulations, making it a suitable choice for self-study. Readers have highlighted the book's strength in simplifying complex ideas surrounding Bayesian processes, making it appealing for those lacking a foundational knowledge in statistics. This aspect positions the book not as an academic text but as a practical guide for applying Bayesian techniques to everyday problems. Many reviewers appreciate the alignment of concepts with actual coding practices, aiding in bridging theoretical knowledge with practical implementation. However, several reviews pointed out some limitations. A number of users noted that the code provided is outdated, specifically being compliant with Python 2.7 rather than the more current 3.x version, leading some learners to seek fixes. Additionally, the absence of a table of contents in the ebook version posed an inconvenience for at least one reader, resulting in a return. In summary, "Think Bayes" is highly recommended for newcomers to Bayesian statistics, especially those intending to apply these concepts in data science. Nevertheless, Prospective readers should be aware of potential challenges regarding outdated coding examples and the ebook's structural shortcomings. **Pros and Cons:** **
Customer Reviews & Ratings
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ventajas
- Clear introduction to Bayesian analysis
- Effective use of examples and Python code
- Suitable for self-study
- Simplifies the Bayes process for practical application
Contras
- Code is outdated (Python 2.7 compliant)
Product Price History
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PEN 173
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características y beneficios
- Designed for Python programmers with a basic understanding of probability.
- Focuses on solving statistical problems through code rather than complex math.
- Covers Bayesian fundamentals clearly through practical examples.
- Teaches computational methods for real-world applications.
- Includes engaging examples like Dungeons & Dragons and sports.
- Ideal for beginners looking to dive into Bayesian statistics.
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