Global Certificate Bayesian Inference R
-- ViewingNowThe Global Certificate Bayesian Inference R Certificate Course is a comprehensive program designed to equip learners with the essential skills required for data analysis in today's data-driven world. This course focuses on the application of Bayesian inference, a powerful and flexible approach to statistical modeling.
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โข Introduction to Bayesian Inference: Basic concepts, principles, and benefits of Bayesian inference. Understanding the Bayes' theorem and its applications.
โข Probability Basics: A refresher on probability theory, including conditional probability, joint probability, and marginal probability.
โข Prior, Likelihood, and Posterior: Defining and understanding prior, likelihood, and posterior distributions, and their significance in Bayesian inference.
โข Conjugate Priors: Introduction to conjugate priors and their importance in Bayesian analysis. Common conjugate prior families like beta, gamma, and normal distributions.
โข Model Selection and Comparison: Methods for comparing and selecting Bayesian models, such as Deviance Information Criterion (DIC) and Bayes factors.
โข Markov Chain Monte Carlo (MCMC): Overview of MCMC methods for sampling from complex posterior distributions, including the Metropolis-Hastings algorithm and Gibbs sampling.
โข Bayesian Computation Tools: Hands-on experience with popular Bayesian computation tools like Stan, PyMC3, and JAGS.
โข Bayesian Hypothesis Testing: Performing hypothesis testing in the Bayesian framework, including credible intervals, highest density intervals, and p-values.
โข Applications of Bayesian Inference: Real-world applications of Bayesian inference in various fields, such as finance, healthcare, engineering, and social sciences.
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