Masterclass Certificate in Bayesian Modeling and Analysis
-- ViewingNowThe Masterclass Certificate in Bayesian Modeling and Analysis is a comprehensive course that imparts critical skills in Bayesian statistics, a rapidly growing field. This course is essential for professionals seeking to enhance their data analysis expertise and stay relevant in today's data-driven world.
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⢠Introduction to Bayesian Modeling: Basic principles, history, and applications of Bayesian modeling. Understanding the Bayes theorem and its role in statistical inference.
⢠Probability Distributions: Overview and properties of common probability distributions used in Bayesian analysis, such as normal, exponential, and multinomial distributions.
⢠Bayesian Inference: Implementing Bayesian inference using various methods like Markov Chain Monte Carlo (MCMC) and Gibbs sampling. Understanding and applying convergence diagnostics for MCMC.
⢠Model Specification and Evaluation: Techniques for specifying and validating Bayesian models, including prior elicitation and model checking.
⢠Linear Regression with Bayesian Analysis: Applying Bayesian methods to linear regression, understanding the role of priors, and interpreting the results.
⢠Generalized Linear Models (GLMs): Extending Bayesian linear regression to GLMs, covering logistic and Poisson regression.
⢠Hierarchical Modeling: Implementing hierarchical models for handling data structures with multiple levels of variation, such as in longitudinal or multilevel data.
⢠Bayesian Nonparametrics: Introduction to nonparametric Bayesian methods, such as Dirichlet processes and Gaussian processes, for flexible modeling of complex data.
⢠Model Selection and Comparison: Techniques for comparing and selecting Bayesian models, including Bayes factors and cross-validation.
⢠Practical Applications and Case Studies: Hands-on experience with real-world datasets and applications, demonstrating the power and versatility of Bayesian modeling and analysis.
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