Brief Introduction
Learn how to construct, fit, estimate and compute Bayesian statistical models with the help of OpenBUGS (freely available software)
Description
Bayesian Statistics is a captivating field and is used most prominently in data sciences. In this course we will learn about the foundation of Bayesian concepts, how it differs from Classical Statistics including among others Parametrizations, Priors, Likelihood, Monte Carlo methods and computing Bayesian models with the exploration of Multilevel modelling.
This course is divided into two parts i.e. Theoretical and Empirical part of Bayesian Analytics. First three weeks cover the Theoretical part which includes how to form a prior, how to calculate a posterior and several other aspects. Rest of the weeks will cover the empirical part which explains how to compute Bayesian modelling. Completion of this course will provide you with an understanding of the Bayesian approach, the primary difference between Bayesian and Frequentist approaches and experience in data analyses.
Knowledge
- Understand the necessary Bayesian concepts from practical point of view for better decision making.
- Learn Bayesian approach to estimate likely event outcomes, or probabilities using datasets.
- Gain “hands on” experience in creating and estimating Bayesian models using R and OPENBUGS.
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