Professional Certificate Bayesian Statistics R
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Course Details
• Introduction to Bayesian Statistics: Basic concepts, principles, and benefits of Bayesian statistics. Understanding probability, likelihood, and prior distributions.
• Probability Distributions in R: Overview of probability distributions, focusing on discrete and continuous distributions in R. Generating random variables and visualizing distributions.
• Bayes' Theorem in R: Implementing Bayes' theorem in R, deriving posterior distributions, and understanding the role of prior and likelihood.
• Conjugate Priors in Bayesian Analysis: Introduction to conjugate priors, selecting appropriate conjugate priors in R, and demonstrating their benefits in Bayesian analysis.
• MCMC & Gibbs Sampling: Overview of Markov Chain Monte Carlo (MCMC) and Gibbs sampling methods, implementing MCMC and Gibbs sampling in R, and understanding their role in Bayesian analysis.
• Model Checking & Comparison: Evaluating and comparing Bayesian models in R, assessing model fit, and ensuring model assumptions are met.
• Bayesian Hierarchical Modeling: Understanding the concept of hierarchical modeling, implementing hierarchical models in R, and interpreting posterior distributions.
• Applications of Bayesian Statistics: Exploring real-world applications of Bayesian statistics in various fields, demonstrating how to apply Bayesian methods to solve complex problems using R.
Career Path
Entry Requirements
- Basic understanding of the subject matter
- Proficiency in English language
- Computer and internet access
- Basic computer skills
- Dedication to complete the course
No prior formal qualifications required. Course designed for accessibility.
Course Status
This course provides practical knowledge and skills for professional development. It is:
- Not accredited by a recognized body
- Not regulated by an authorized institution
- Complementary to formal qualifications
You'll receive a certificate of completion upon successfully finishing the course.
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