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training duration icon R - Bayesian Statistics using R

Duration: 3 Days
Delivery Type: Classroom
Target Audience: Data Analysts
Course Number: 1000031
Method: Presentation with examples and hands-on labs.
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A. Bayesian Statistics

[Duration: 0.5 Days] Introduction: Quantifying Uncertainty Using Probabilities, Models and Prior Probabilities, Likelihoods and Posterior Probabilities, Bayesian Sequential Analysis - Review of Probability: Events and Sample Spaces, Unions - Intersections, Complements - Marginal and Conditional Probabilities - Bayes’ Rule - Addition and Multiplication Rules

B. One-Parameter Models

[Duration: 0.5 Days] Bayesian Models - Prior Probability and Prior Distributions - The Posterior Distribution - Conjugate Priors - Inference for a Population Proportion: Frequentist Approach, Bayesian Inference, Bayesian Point Estimates - R for Bayesian Analysis - Inference Using Nonconjugate Priors on Mean and Variance - Noninformative Priors

C. Multiparameter Models

[Duration: 0.25 Days] Informative Priors for Mean and Variance - Conjugate Joint Prior Density for Mean and Variance

D. Model Fit using Markov Chain Monte Carlo (MCMC)

[Duration: 0.5 Days] Sampling-Based Methods - Markov Chain Monte Carlo (MCMC) Methods - Bayesian Models - Hierarchical Models: Fitting Bayesian Hierarchical Models, Estimation Based on Hierarchical Models - Software OpenBUGS

E. Regression and Hierarchical Regression Models

[Duration: 0.5 Days] Review of Linear Regression - Introduction to Bayesian Simple Linear Regression - Generalized Linear Models - Hierarchical Normal Linear Models - Model Comparison, Model Checking, and Hypothesis Testing - Bayes Factors for Model Comparison and Hypothesis Testing - Bayes Factors and Bayesian Hypothesis Testing

F. Data Mining and Classification in Bayesian Statistics

[Duration: 0.75 Days] Statistics for Machine Learning - Learning as Inference - Principal Components Analysis - Naive Bayes - Nearest Neighbour Classification - Gaussian Processes

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For trainings with hands-on labs you are kindly requested to bring your own laptop with the required software. Alternatively, you can use our virtual machines (VMWare) for the MS Windows OS.
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