Bayesian Modeling with RJAGS

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Course Features

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Duration

4 hours

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Delivery Method

Online

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Available on

Limited Access

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Accessibility

Mobile, Desktop, Laptop

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Language

English

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Subtitles

English

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Level

Intermediate

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Teaching Type

Self Paced

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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

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Virtual Labs

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International Faculty

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Post Course Interactions

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Hands-On Training,Instructor-Moderated Discussions

Skills You Will Gain

Prerequisites/Requirements

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Fundamentals of Bayesian Data Analysis in R

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Introduction to the Tidyverse

What You Will Learn

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

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

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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 Instructors

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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...

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Course Cover

$12

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49% OFF

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