Hands On Training

Bayesian Modeling with RJAGS

via

DataCamp

Course Features

Duration

4 hours

Delivery Method

Online

Available on

Limited Access

Accessibility

Mobile, Desktop, Laptop

Language

English

Subtitles

English

Level

Intermediate

Teaching Type

Self Paced

Video Content

4 hours

Course Description

Machine learning and Bayesian statistics are intuitive, flexible, and logical. This course will show you how to create and analyze generalizable Bayesian model. These models can be used for simple, one-parameter models as well as intermediate multivariate generalized linear regression models. Because of the availability and affordability of computing resources, Bayesian models are becoming more popular. The rjags package is one of these resources. It can be found in R.

Course Overview

Virtual Labs

International Faculty

Post Course Interactions

Hands-On Training,Instructor-Moderated Discussions

Skills You Will Gain

Prerequisites/Requirements

Introduction to the Tidyverse

Fundamentals of Bayesian Data Analysis in R

What You Will Learn

In this course, you will engineer and analyze a family of foundational, generalizable Bayesian models

In this course, you'll learn how to implement more advanced Bayesian models using RJAGS

You will utilize one of these resources - the rjags package in R. Combining the power of R with the JAGS (Just Another Gibbs Sampler) engine, rjags provides a framework for Bayesian modeling, inference, and prediction

Course Content

Expand all sections

Module 1: Introduction to Bayesian Modeling

Module 2: Bayesian Models Markov Chains

Module 3: Bayesian Inference Prediction

Module 4: Multivariate Generalized Linear Models

Course Instructors

Alicia Johnson

Associate Professor, Macalester College

Professor Johnson primary research interest is in Markov chain Monte Carlo (MCMC) methods. The focus of her research has been on the convergence rates of chains corresponding to MCMC algorithms on ge...