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Bayesian Programming for Beginners: Learn to Build, Train, and Apply Bayesian Networks in Real Projects
Paperback
Publisher: Independently Published
Published: Mar 29 2026
Pages: 162
Weight: 0.64
Height: 0.35 Width: 7.00 Depth: 10.00
Language: English
In today's data-driven world, real problems are rarely clear-cut. From predicting user behavior to diagnosing diseases or detecting fraud, uncertainty is everywhere. This book teaches you how to embrace that uncertainty and turn it into a powerful advantage.
Designed specifically for developers and beginners, this book takes you step by step from the fundamentals of probability to building real-world Bayesian models using Python. You don't need a strong math background-everything is explained in clear, simple language with fully working code examples you can run immediately.
Inside, you'll learn how to:
- Understand probability, conditional probability, and Bayes' theorem
- Build and structure Bayesian Networks from scratch
- Perform probabilistic inference to make data-driven predictions
- Learn model parameters and structures directly from data
- Use Python libraries like pgmpy for real-world implementations
- Apply Bayesian methods to healthcare, finance, and AI systems
- Scale and optimize models for practical use
By the end of this book, you'll be able to design and implement your own Bayesian models for real-world applications such as fraud detection, recommendation systems, and decision support tools.
Whether you're a developer, data analyst, student, or AI enthusiast, this book will give you a solid foundation in probabilistic thinking and Bayesian programming.
If you want to build smarter, more explainable AI systems that work in the real world, this book will show you how.
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