Overview of Bayesian Approach to Statistical Methods
Preface
Statistical methods are being used in different fields such as Business & Economics, Engineering, Clinical & Pharmaceutical research including the emerging fields such as Machine Learning and Artificial Intelligence. Statistical methods based on the traditional frequentist approach are currently being use in these fields. With the emergence of high end computing nowadays Bayesian approach to Statistical Methods also being used in different fields.
Bayesian approach involves prior, likelihood and posterior concepts in carrying out the statistical analysis. Bayesian methods assume model parameters as random as opposed to fixed in frequentist approach. It is useful even when the sample size is small. One of the drawbacks of Bayesian method is it involves subjectivity in carrying out the analysis.
With the availability of advanced computing technologies, implementation of Bayesian methods is possible using Markov Chain Monte Carlo (MCMC) methods.
This book provides an overview of Bayesian approaches to statistical methods and uses open source software R for carrying out analysis using sample data sets which can be downloaded from author’s website.
Title: Overview of Bayesian Approach to Statistical Methods
Author: Vinaitheerthan Renganathan
Date of Publication: 23-Mar-2022
ISBN: 9789356201187
Purchase Link:
https://www.amazon.com/dp/B09WB7378Q
https://www.amazon.com/dp/9356201188
www.vinaitheerthan.com/book.php
vinaibook@gmail.com
2. Statistical methods are being used in different fields
such as Business & Economics, Engineering, Clinical &
Pharmaceutical research including the emerging fields
such as Machine Learning and Artificial Intelligence.
Statistical methods based on the traditional frequentist
approach are currently being use in these fields. With
the emergence of high end computing nowadays
Bayesian approach to Statistical Methods also being
used in different fields.
Introduction
3. Bayesian approach involves prior, likelihood and posterior
concepts in carrying out the statistical analysis. Bayesian
methods assume model parameters as random as opposed
to fixed in frequentist approach. It is useful even when the
sample size is small. One of the drawbacks of Bayesian
method is it involves subjectivity in carrying out the analysis.
With the availability of advanced computing technologies,
implementation of Bayesian methods is possible using
Markov Chain Monte Carlo (MCMC) methods.
Introduction
4. This book provides an overview of Bayesian approaches
to statistical methods and uses open source software R
for carrying out analysis using sample data sets which
can be downloaded from author’s website.
Introduction
5. Chapter 1: Introduction
Chapter 2: Frequentist and Bayesian Approach to Statistical
Methodology
Chapter 2: R Software
Chapter 3: Probability and Probability Distribution
Chapter 4: Statistical Tests
Chapter 5: Regression Modeling
Chapter 6: Bayesian Survival Analysis
Chapter 7: Bayesian Network
Chapter 8: Bayesian Classification Model
Chapter 9: Bayesian methodology other applications
Table of Contents
