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Sinopsis
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
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Beyond Fixed Models (Paperback)
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Paperback. Condición: new. Monroe, Eline Ilustrador. 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 multiple locations in the US or from the UK, depending on stock availability. Nº de ref. del artículo: 9798266523968
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Beyond Fixed Models: Bayesian Non-Parametrics for Data Science
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Condición: New. Monroe, Eline Ilustrador. Print on Demand. Nº de ref. del artículo: I-9798266523968
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Beyond Fixed Models
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Beyond Fixed Models
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PAP. Condición: New. Monroe, Eline Ilustrador. New Book. Shipped from UK. Established seller since 2000. Nº de ref. del artículo: L2-9798266523968
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Beyond Fixed Models (Paperback)
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Paperback. Condición: new. Monroe, Eline Ilustrador. 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. Nº de ref. del artículo: 9798266523968
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Beyond Fixed Models : Bayesian Non-Parametrics for Data Science
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Createspace Independent Publishing Platform Sep 2025, 2025
ISBN 13: 9798266523968
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Taschenbuch. Condición: Neu. Monroe, Eline Ilustrador. Neuware - 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. Nº de ref. del artículo: 9798266523968
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