Bayesian Inference on Complicated Data

Edited by Niansheng Tang

in Books from IntechOpen

Abstract: 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.

JEL-codes: C60 (search for similar items in EconPapers)
Date: 2020
ISBN: 978-1-83880-385-8
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Book downloadable chapter-by-chapter

Chapters in this book:

A Brief Tour of Bayesian Sampling Methods

Michelle Yongmei Wang and Trevor Park

A Review on the Exact Monte Carlo Simulation

Hongsheng Dai

Bayesian Analysis for Random Effects Models

Junshan Shen and Catherine Chunling Liu

Bayesian Inference of Gene Regulatory Network

Xi Chen and Jianhua Xuan

On the Impact of the Choice of the Prior in Bayesian Statistics

Fatemeh Ghaderinezhad and Christophe Ley

Patient Bayesian Inference: Cloud-Based Healthcare Data Analysis Using Constraint-Based Adaptive Boost Algorithm

Shahid Naseem

The Bayesian Posterior Estimators under Six Loss Functions for Unrestricted and Restricted Parameter Spaces

Ying-Ying Zhang

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Persistent link: https://EconPapers.repec.org/RePEc:ito:pbooks:6440

DOI: 10.5772/intechopen.83214

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