Postgraduate Certificate in Applied Bayesian Analysis
-- ViewingNowThe Postgraduate Certificate in Applied Bayesian Analysis is a comprehensive course designed to equip learners with the essential skills needed to excel in the data analysis industry. This program focuses on the application of Bayesian methods, which are increasingly in demand due to their ability to provide robust solutions to complex data problems.
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⢠Introduction to Bayesian Analysis: Basic principles, probability theory, Bayes' theorem, and the concept of prior and posterior distributions.
⢠Probability Distributions: Overview of common probability distributions used in Bayesian analysis, such as normal, binomial, Poisson, and exponential distributions.
⢠Bayesian Inference: Methods for making inferences based on observed data, including point estimation, credible intervals, and hypothesis testing.
⢠Modeling with Graphical Models: Introduction to directed acyclic graphs (DAGs), plate notation, and how to specify models using these graphical representations.
⢠Markov Chain Monte Carlo (MCMC): Overview of MCMC methods, including the Metropolis-Hastings algorithm and Gibbs sampling, and their application in Bayesian analysis.
⢠Model Checking and Comparison: Methods for checking the fit of Bayesian models, including posterior predictive checks, and comparing the performance of different models using information criteria.
⢠Hierarchical Models: Introduction to hierarchical models, including random effects models, and their application in practice.
⢠Bayesian Computing: Hands-on experience with Bayesian computing software, such as Stan or JAGS, for implementing Bayesian models.
⢠Case Studies in Applied Bayesian Analysis: Application of Bayesian methods to real-world problems, including examples from fields such as finance, healthcare, and social sciences.
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