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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.
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.
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