Learn the concepts and practical side of using the Bayesian approach to estimate likely event outcomes.
Master key concepts and practical skills through structured learning modules. By completing this curriculum, you'll gain valuable expertise applicable to real-world scenarios.
Understand Bayesian concepts, and gain a great deal of practical "hands-on" experience creating and estimating Bayesian models using R software.
Effectively use the Bayesian approach to estimate likely event outcomes, or probabilities, using their own data.
Be able to apply a range of Bayesian functions using R software in order to model and estimate single parameter, multi-parameter, conjugate mixture, multinomial, and rejection and importance sampling Bayesian models.
Understand and use both predictive priors and predictive posteriors in Bayesian applications.
Be able to compare and evaluate alternative, competing Bayesian models.
This comprehensive Bayesian Computational Analyses with R curriculum is designed to take you from foundational concepts to advanced implementation. Each module builds upon the previous, ensuring a structured learning path that maximizes knowledge retention and practical application.
Understand what you need to succeed in this course and determine if it's the right fit for your learning goals
What you need before starting this Bayesian Computational Analyses with R course:
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Everything you need to know about this online course, from duration to certification
Course Instructor
Geoffrey Hubona, Ph.D.
Expert instructor with industry experience
Course Language
English
All materials in English
This online course offers comprehensive training with expert instruction, practical exercises, and a certificate of completion. Join thousands of students advancing their careers through quality online education.
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