Certificate Bayesian Statistics R Fundamentals
-- ViewingNowCertificate Bayesian Statistics R Fundamentals: This course is critical for professionals seeking to enhance their statistical analysis skills with Bayesian methods. The curriculum covers fundamental Bayesian theory, R programming, and practical applications in various industries.
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⢠Introduction to Bayesian Statistics – Basic concepts, differences from classical statistics, and real-world applications.
⢠Probability Theory – A review of probability theory, including joint and conditional probability, and Bayes' theorem.
⢠Priors, Posteriors, and Likelihoods – Understanding the components of Bayes' theorem, their roles, and mathematical representations.
⢠Conjugate Priors – Introduction and applications of conjugate priors in Bayesian analysis.
⢠Bayesian Inference – Process of making inferences based on data and prior beliefs.
⢠Gibbs Sampling and MCMC – Markov Chain Monte Carlo (MCMC) methods, with a focus on Gibbs sampling for posterior approximation.
⢠Model Checking and Comparison – Techniques for checking model assumptions and comparing different models.
⢠R Packages for Bayesian Inference – Overview of essential R packages for Bayesian statistics, such as rstan, BayesFactor, and greta.
⢠Case Studies – Applying Bayesian methods to practical problems in R.
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