Beyond Fixed Models (Paperback)

Eline Monroe

ISBN 13: 9798266523968

Published by Independently Published, 2025

About this Item

Paperback. Beyond Fixed Models: Bayesian Nonparametrics for Data Science is a comprehensive guide for data scientists, statisticians, and researchers who want to move beyond traditional parametric models. This book introduces Bayesian nonparametric methods and demonstrates how they can be applied to complex, real-world data challenges in a practical and accessible way.Readers will learn how to use flexible, data-driven models for tasks such as clustering, regression, time-series analysis, and spatial modeling. The book covers foundational topics like Bayesian inference and Dirichlet processes, moves into advanced techniques including Gaussian processes and hierarchical models, and explores cutting-edge methods like neural processes and deep kernel integration with machine learning pipelines.With clear workflows, reproducible examples, and guidance on computational tools such as Stan, PyMC, and GPflow, this book equips readers to build robust, scalable models while addressing interpretability and practical challenges in modern data science.Key Features: Practical tutorials for implementing Bayesian nonparametric methodsGuidance on scalable computation and handling large datasetsApplications for clustering, regression, time-series, and spatial modelingStep-by-step explanations of Dirichlet processes, Gaussian processes, and hierarchical modelsIntegration of advanced techniques with modern machine learning pipelines This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Seller Inventory # 9798266523968

Bibliographic Details

• Title: Beyond Fixed Models (Paperback)
• Publisher: Independently Published
• Publication Date: 2025
• Language: English
• ISBN 13: 9798266523968
• Binding: Paperback
• Illustrator: Monroe, Eline
• Condition: new

About this title

Synopsis

Beyond Fixed Models: Bayesian Nonparametrics for Data Science is a comprehensive guide for data scientists, statisticians, and researchers who want to move beyond traditional parametric models. This book introduces Bayesian nonparametric methods and demonstrates how they can be applied to complex, real-world data challenges in a practical and accessible way.

Readers will learn how to use flexible, data-driven models for tasks such as clustering, regression, time-series analysis, and spatial modeling. The book covers foundational topics like Bayesian inference and Dirichlet processes, moves into advanced techniques including Gaussian processes and hierarchical models, and explores cutting-edge methods like neural processes and deep kernel integration with machine learning pipelines.

With clear workflows, reproducible examples, and guidance on computational tools such as Stan, PyMC, and GPflow, this book equips readers to build robust, scalable models while addressing interpretability and practical challenges in modern data science.

Key Features:

• Practical tutorials for implementing Bayesian nonparametric methods

• Guidance on scalable computation and handling large datasets

• Applications for clustering, regression, time-series, and spatial modeling

• Step-by-step explanations of Dirichlet processes, Gaussian processes, and hierarchical models

• Integration of advanced techniques with modern machine learning pipelines