Applied Bayesian for Analytics

Por: Swayam . en: , ,

Week 01: What is Bayesian Statistics and How it is different than Classical Statistics
  • Foundations of Bayesian Inference
  • Bayes theorem
  • Advantages of Bayesian models
  • Why Bayesian approach is so important in Analytics
  • Major densities and their applications
Week 02: Bayesian analysis of Simple Models
  • Likelihood theory and Estimation
  • Parametrizations and priors
  • Learning from binary models
  • Learning from Normal Distribution
Week 03: Monte Carlo Methods
  • Basics of Monte carol integration
  • Basics of Markov chain Monte Carlo
  • Gibs Sampling
Week 04: Computational Bayes
  • Examples of Bayesian Analytics
  • Introduction to R and OPENBUGS for Bayesian analysis
Week 05: Bayesian Linear Models
  • Context for Bayesian Regression Models
  • Normal Linear regression
  • Logistic regression
Week 06: Bayesian Hierarchical Models
  • Introduction to Multilevel models
  • Exchangeability
  • Computation in Hierarchical Models

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