Advanced Certificate Bayesian Statistics R Integration
-- ViewingNowThe Advanced Certificate Bayesian Statistics R Integration course is a comprehensive program designed to provide learners with in-depth knowledge of Bayesian statistics and its integration with R, a powerful statistical software. This course is crucial in today's data-driven world, where businesses are seeking professionals who can analyze and interpret complex data to make informed decisions.
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โข Bayesian Inference Review · Understanding the basics of Bayesian inference, including probability theory, prior and posterior distributions, and Bayes' theorem.
โข Probability Distributions in R · Learning to implement various probability distributions in R, including normal, exponential, and binomial distributions.
โข Bayesian Hierarchical Modeling · Exploring multilevel modeling techniques for incorporating hierarchical structures in Bayesian models.
โข Markov Chain Monte Carlo (MCMC) Methods · Understanding the principles of MCMC methods, such as the Metropolis-Hastings algorithm and Gibbs sampling, for estimating posterior distributions.
โข Stan & R Integration · Integrating Stan with R for advanced Bayesian modeling, including model specification and diagnostics.
โข Bayesian Networks in R · Learning to construct and analyze Bayesian networks in R, including conditional probability tables and the use of the gRain package.
โข Model Selection & Comparison · Understanding methods for comparing and selecting Bayesian models, including the Deviance Information Criterion (DIC) and cross-validation.
โข Bayesian Nonparametrics · Exploring nonparametric Bayesian methods, such as Dirichlet processes and Gaussian processes, for modeling complex data structures.
โข Bayesian Time Series Analysis · Learning to model time series data using Bayesian methods, including the use of the bsts package in R.
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