- 118 pages
- English
- PDF
- Available on iOS & Android
eBook - PDF
Bayesian Inference on Complicated Data
About this book
Due to great applications in various fields, such as social science, biomedicine, genomics, and signal processing, and the improvement of computing ability, Bayesian inference has made substantial developments for analyzing complicated data. This book introduces key ideas of Bayesian sampling methods, Bayesian estimation, and selection of the prior. It is structured around topics on the impact of the choice of the prior on Bayesian statistics, some advances on Bayesian sampling methods, and Bayesian inference for complicated data including breast cancer data, cloud-based healthcare data, gene network data, and longitudinal data. This volume is designed for statisticians, engineers, doctors, and machine learning researchers.
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Please note we cannot support devices running on iOS 13 and Android 7 or earlier. Learn more about using the app.
Yes, you can access Bayesian Inference on Complicated Data by Niansheng Tang in PDF and/or ePUB format, as well as other popular books in Mathematics & Applied Mathematics. We have over one million books available in our catalogue for you to explore.
Information
Table of contents
- Bayesian Inference on Complicated Data
- Contents
- Preface
- Section 1 - The Choice of the Prior
- Chapter 1 - On the Impact of the Choice of the Prior in Bayesian Statistics
- Section 2 - Some Advances on Sampling Methods
- Chapter 2 - A Brief Tour of Bayesian Sampling Methods
- Chapter 3 - A Review on the Exact Monte Carlo Simulation
- Section 3 - Bayesian Inference for Complicated Data
- Chapter 4 - Bayesian Analysis for Random Effects Models
- Chapter 5 - Bayesian Inference of Gene Regulatory Network
- Chapter 6 - Patient Bayesian Inference: Cloud-Based Healthcare Data Analysis Using Constraint-Based Adaptive Boost Algorithm
- Chapter 7 - The Bayesian Posterior Estimators under Six Loss Functions fo Unrestricted and Restricted Parameter Spaces