Bayesian Nonparametrics

✍ Scribed by J.K. Ghosh, R.V. Ramamoorthi

Publisher: Springer
Year: 2003
Tongue: English
Leaves: 318
Series: Springer series in statistics
Category: Library

⬇ Acquire This Volume

No coin nor oath required. For personal study only.

📜 SIMILAR VOLUMES

Bayesian Nonparametrics

📁 Bayesian Nonparametrics

✍ J.K. Ghosh, R.V. Ramamoorthi 📂 Library 📅 2003 🏛 Springer 🌐 English

Bayesian nonparametrics has grown tremendously in the last three decades, especially in the last few years. This book is the first systematic treatment of Bayesian nonparametric methods and the theory behind them. While the book is of special interest to Bayesians, it will also appeal to statisticia

Bayesian nonparametrics

📁 Bayesian nonparametrics

✍ Hjort N.L., et al. (eds.) 📂 Library 📅 2010 🏛 CUP 🌐 English

Bayesian nonparametrics works - theoretically, computationally. The theory provides highly flexible models whose complexity grows appropriately with the amount of data. Computational issues, though challenging, are no longer intractable. All that is needed is an entry point: this intelligent book is

Bayesian Nonparametrics

📁 Bayesian Nonparametrics

✍ Nils Lid Hjort, Chris Holmes, Peter Müller, Stephen G. Walker 📂 Library 📅 2010 🏛 Cambridge University Press 🌐 English

Bayesian Nonparametrics

📁 Bayesian Nonparametrics

✍ Nils Lid Hjort, Chris Holmes, Peter Müller, Stephen G. Walker 📂 Library 📅 2010 🏛 Cambridge University Press 🌐 English

Bayesian Nonparametrics

📁 Bayesian Nonparametrics

✍ J.K. Ghosh, R.V. Ramamoorthi 📂 Library 📅 2003 🏛 Springer 🌐 English

The book is primarily aimed at graduate students and can be used as the text for a graduate course in Bayesian nonparametrics. Though the emphasis of the book is on nonparametrics, there is a substantial chapter on asymptotics of classical Bayesian parametric models.

Bayesian Nonparametric Data Analysis

📁 Bayesian Nonparametric Data Analysis

✍ Peter Müller, Fernando Andres Quintana, Alejandro Jara, Tim Hanson (auth.) 📂 Library 📅 2015 🏛 Springer International Publishing 🌐 English

<p>This book reviews nonparametric Bayesian methods and models that have proven useful in the context of data analysis. Rather than providing an encyclopedic review of probability models, the book’s structure follows a data analysis perspective. As such, the chapters are organized by traditional dat