Bayesian Data Analysis Course 2018-02-13T13:38:58+00:00

Bayesian Data Analysis Course

Session 2: June 11 – 15, 2018
Albuquerque, NM – Embassy Suites

FAQVenue Info
$1,795 Faculty/Professional or $1,095 Student/Post-Doc

Payment Options
Per Course Qty
Professionalshow details + $1,795 (USD)  
Studentshow details + $1,095 (USD)  

Statistics Course Objectives

The five-day training institute on Bayesian Data Analysis will enable participants to:

  • The rich information provided by Bayesian analysis and how it differs from traditional (Frequentist) statistical analysis
  • The concepts of Bayesian reasoning along with the easy math and intuitions for Bayes’ rule
  • The concepts and hands-on use of modern algorithms (“Markov chain Monte Carlo”) that achieve Bayesian analysis for realistic applications
  • How to use the free software R and JAGS for Bayesian analysis, with many programs created by the instructor, readily useable and adaptable for your research applications
  • An extensive array of applications, including comparison of two groups, ANOVA-like designs, linear regression, logistic regression, ordinal regression, etc. Also numerous variations for robustness to outliers, non-normally distributed noise, heterogenous variances, censored data, non-linear trends, auto-regressive models, etc.

Course Syllabus

Summer Stats Camp 2018: Bayesian Data Analysis
Monday June 11, 2018
9:00–10:45 Bayesian reasoning generally
10:45–11:00 Snack and Refreshment Break
11:00–12:30 Perfidious pp values and the con game of confidence intervals
12:30–1:30 Lunch Break
1:30–3:15 Bayes’ rule, grid approximation, and R; Simple examples to train intuition
3:15–3:30 Snack and Refreshment Break
3:30–5:00 Markov Chain Monte Carlo and JAGS; Complete programs for Bayesian analysis
Tuesday June 12, 2018
9:00–10:45 Hierarchical models; Shrinkage; Examples: therapeutic touch, baseball, meta-analysis of extrasensory perception
10:45–11:00 Snack and Refreshment Break
11:00–12:30 The generalized linear model. Simple linear regression; exponential regression; sinusoidal regression, with autoregression component – Robust versions of all
12:30–1:30 Lunch Break
1:30–3:15 How to modify a program in JAGS & rjags for a different model: Multiple linear regression, Logistic regression, Ordinal regression
3:15–3:30 Snack and Refreshment Break
3:30–5:00 Hierarchical regression models: Estimating regression parameters at multiple levels simultaneously
Wednesday June 13, 2018
9:00–10:45 Hierarchical model for shrinkage of regression coefficients in multiple regression
10:45–11:00 Snack and Refreshment Break
11:00–12:30 Bayesian variable selection in multiple regression
12:30–1:30 Lunch Break
1:30–3:15 Model comparison as hierarchical model – The Bayes factor
3:15–3:30 Snack and Refreshment Break
3:30–5:00 Two Bayesian ways to assess null values: Estimation vs model comparison
Thursday June 14, 2018
9:00–10:45 Bayesian hierarchical oneway “ANOVA” – Comparisons and shrinkage
10:45–11:00 Snack and Refreshment Break
11:00–12:30 Models for unequal variances (“heteroscedasticity”)
12:30–1:30 Lunch Break
1:30–3:15 Bayesian hierarchical two way “ANOVA” with interaction – Interaction contrasts
3:15–3:30 Snack and Refreshment Break
3:30–5:00 Log-linear models and chi-square test
Friday June 15, 2018
9:00–10:45 Power: Probability of achieving the goals of research
10:45–11:00 Snack and Refreshment Break
11:00–12:30 The goal of achieving precision instead of rejecting/accepting a null value. How to report a Bayesian analysis. Advanced topics as time permits
12:30–1:30 Lunch Break
1:30~3:30 Individual Consultations

Why Should You Attend?

  • Get 1 on 1 Consultation With Instructor
  • Professional Networking
  • Peer Socializing
  • Collaboration
  • All Course Resources
  • Breakfast (Embassy guests), Lunches, & Snacks Daily

Statistical Methods Course Description

Overview: Many fields of science are transitioning from null hypothesis significance testing (NHST) to Bayesian data analysis. Bayesian analysis provides rich information about the relative credibilities of all candidate parameter values for any descriptive model of the data, without reference to p values. Bayesian analysis applies flexibly and seamlessly to complex hierarchical models and realistic data structures, including small samples, large samples, unbalanced designs, missing data, censored data, outliers, etc. Bayesian analysis software is flexible and can be used for a wide variety of data-analytic models. (More about why to go Bayesian is described below.) This course shows you how to do Bayesian data analysis, hands on, with free software called R and JAGS. The course will use new programs and examples.


Mike Kalish Statistics CourseMike Kalish is a Professor of Psychology at Syracuse University. He earned a PhD in Cognitive Science from UC San Diego and has held academic positions in both the US and Australia. His primary research area is the psychology of category learning, which has led him to work on areas of methodology including State-Trace Analysis and Bayesian estimation and model selection. He frequently teaches postgraduate level courses on Bayesian statistics.

Course Audience

The intended audience is advanced students, faculty, and other researchers, from all disciplines, who want a groundfloor introduction to doing Bayesian data analysis.

Course Files

Below are links to course files for those who enrolled in the course. Please download these files onto your computer before the first day of the course. The files are password protected to respect the intellectual property rights of the instructors. By using your login information you agree not to share your login information or the content protected by it.

After May 1st: Click Here to Access Bayesian Data Analysis Course Files

Bayesian Data Analysis Statistics Course