Applied Bayesian Analysis with R

Partner: Udemy
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Description: This course provides a comprehensive, hands-on approach to Bayesian statistics, focusing on fundamental concepts and practical applications using R. Designed for beginners and those with some statistical background, this course will guide you through the core principles of Bayesian analysis, allowing you to understand and apply these methods to real-world data.Course StructureLecture 1: Why Bayes? Introduction and WelcomeWe start with a fundamental question: Why Bayesian statistics? This lecture introduces the advantages of Bayesian thinking, contrasting it with frequentist methods to highlight how Bayesian analysis provides a flexible, intuitive approach to data. This session sets the stage for understanding the Bayesian perspective and what you can expect to gain from this course.Lecture 2: R Setup for Bayesian StatisticsIn this session, we’ll set up R for Bayesian analysis, covering essential packages and libraries, and walk through basic commands for data manipulation and visualization. By the end, you'll be equipped with the tools needed to dive into Bayesian modeling.Lecture 3: The Bayesian Trinity: Priors, Likelihood, and PosteriorsHere, we explore the three central components of Bayesian analysis: priors, likelihood, and posteriors. We’ll discuss how these elements interact to shape Bayesian inference and will use R to visualize how prior beliefs combine with data to form posterior distributions.Lecture 4: Bayesian Regression in RThis lecture delves into Bayesian regression, covering linear models in a Bayesian framework. You'll learn how to specify priors, compute posterior distributions, and interpret results, building on classical regression knowledge to gain a Bayesian perspective.Lecture 5: Logistic Regression and PredictionsExpanding on regression techniques, this session introduces Bayesian logistic regression, ideal for binary outcomes and classifica
Category: Development > Data Science > Statistical Modeling
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Price: 99.99
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Source: Impact
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