Bayesian Statistics Online Course

In this Mastery Series (you select 3 of 5 courses), you’ll learn exactly how to percreate Bayesian analysis with BUGS software program package by applying Markov Chain Monte Carlo techniques to Bayesian statistical modeling. You’ll additionally learn to employ RJags and also Rstan, programs for Bayesian analysis within R.

You watching: Bayesian statistics online course

In this 3-course Mastery Series, you"ll learn exactly how to perform Bayesian evaluation with BUGS software application package by using Markov Chain Monte Carlo (MCMC) approaches to Bayesian statistical modeling. You"ll additionally learn to employ RJags and Rstan, programs for Bayesian evaluation within R. This regime combines theoretical sessions and real-people applications to offer you the devices to use Bayesian methods in your very own job-related.


*

*


*

See more: Mizzou Online Course - Missouri Online: Home

*

Enroll in our Bayesian fairtradeexpo.org Mastery Series if you are a student or expert desires a deep dive in the Bayesian techniques in order to build statistical software application and data evaluation. A Record of Mastery will certainly give you the edge you need to enhance your career or the skills you have to find a new task. Related project titles include:
When you earn your Record of Mastery in Bayesian fairtradeexpo.org, you will certainly have actually an thorough and also handy expertise of Bayesian approaches to construct statistical models that incorporate prior judgments or information.
Explain the benefits of Bayesian approaches and also the fundamental principle of MCMC Install BUGS software application and also be able to compose BUGS code Work with 3 traditional schemes for prior and also posterior distributions Conduct posterior recaps and also tests Make predictions from models Conduct Bayesian regression Specify 3-phase Bayesian ordered version Meacertain model fit and also inspect parameters Model the variance/covariance in Bayesian random results models Apply Bayesian ordered models to meta-analysis Specify multi-level and panel models Manage overdispersion for count and propercent data
Perdevelop Bayesian analysis for a binomial proportion and also a normal suppose Perform Bayesian evaluation for distinctions in proparts and indicates Percreate Bayesian evaluation for a basic straight regression Contrast Bayesian techniques with frequentist approaches Write code in rjags Specify models for straight regression; count, binary and also binomial data Specify priors and also likelihoods to specify a model Install rstan and also create code in R Implement a straight regression model in rstan Implement logistic, Poischild, ordinal and weighted regression in rstan