Idézet:Python Probabilistic Programming with PyMC & ArviZ: A Practical Guide to Bayesian Modeling, Inference, and Real-World Applications

Master Python Probabilistic Programming for Real-World Data Science and Bayesian Analysis
If you want to level up your data science skills and make better, data-driven decisions, Python Probabilistic Programming with PyMC & ArviZ is your complete, practical guide. Perfect for beginners and professionals alike, this book covers everything from Bayesian statistics with Python to advanced probabilistic graphical models-all through clear explanations and hands-on projects.
Using PyMC3 and ArviZ tutorials, you'll learn step-by-step how to perform Bayesian inference for beginners, design powerful statistical modeling with Python, and solve complex problems with machine learning using Bayesian methods. No endless theory-just real-world, actionable skills.
Inside, you'll discover how to
Build and analyze probabilistic graphical models in Python for predictive insights.
Apply Bayesian data analysis with Python to real datasets.
Use Python for statistical inference and uncover patterns in your data.
Master advanced Python statistical programming for decision-making under uncertainty.
Work through practical probabilistic programming Python examples from start to finish.
Combine Python data analysis and modeling skills to create complete data science workflows.
Whether you're a student, developer, or data analyst, this book will help you confidently apply Bayesian methods to Python data science projects and turn raw numbers into meaningful results.

Idézet:A kódrészlet megtekintéséhez be kell jelentkezned, vagy nincs jogosultságod a tartalom megtekintéséhez.

Idézet:A kódrészlet megtekintéséhez be kell jelentkezned, vagy nincs jogosultságod a tartalom megtekintéséhez.