//Bayesian Data Analysis Training Seminar
Bayesian Data Analysis Training Seminar 2018-02-13T14:37:57+00:00

Bayesian Data Analysis

Bayesian Data Analysis Seminar

This is an intermediate 3-day course on Bayesian Data Analysis.

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. This course shows you how to do Bayesian data analysis, hands on, with free software called R and JAGS.

World Camp: April 26 – 28, 2018
Brisbane, Australia – Marriott 


FAQ –  Hotel Reservations

Travel Visa Required: Please follow link below to attain a visa for the purposes of “participating in a conference, trade fair, or seminar”. Visa application requires 24hr processing time, please apply early. You will need to apply here estimated cost is 20AUD.

Address: 515 Queen Street, Brisbane QLD 4000
Last day to book: 10 April 2018 or until block is filled
Cancellation: 100% cancellation fee will apply if cancelled 14 days prior.
Phone: 07 – 3303 – 8000
Group Rate Start date: 24 April 2018
Group Rate End date:  30 April 2018

Rooms Options Available:

Deluxe City View Room for AU$229  per room per night + additional taxes and fees, Room including Wifi and Breakfast for 1 Guest.

Deluxe City View Room for AU$249  per room per night + additional taxes and fees, Room including Wifi and Breakfast for 2 Guests.

Hotel Reservations: Follow link above or call the Reservations
Team at 07 – 3303 – 8000 and quote YHA group code.

Payment Options

$1,095 Faculty/Professional or $945 Student/Post-Doc

Per Seminar Qty
Professionalshow details + $1,095 (USD)   Sold Out
Studentshow details + $945 (USD)   Sold Out

Topics include:

  • 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.

Learning Objectives

After engaging in this 3-day statistics training institute course lectures and discussions as well as completing the hands-on practice activities with real data, participants will be able 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, readily useable and adaptable for your research applications
  • An extensive array of applications, including comparison of two groups, ANOVA-like designs, linear regression, and logistic
  • How to apply Bayesian estimation to hierarchical (multi-level) models. See more details in the list of topics,

Syllabus

Thursday April 26, 2018
9:00–10:45 Bayesian reasoning generally
10:45–11:00 Snack and Refreshment Break
11:00–12:30 Perfidious p values and the con game of confidence intervals
12:30–1:30 Lunch Break
1:30–3:15 Bayes’ rule, and R; Simple examples to train intuition
3:15–3:30 Snack and Refreshment Break
3:30–5:00 Markov Chain Monte Carlo; R programs for Bayesian analysis
Friday April 27, 2018
9:00–10:45 HDI, ROPE, decision rules, and null values
10:45–11:00 Snack and Refreshment Break
11:00–12:30 General linear model. Multiple regression, and robust regression
12:30–1:30 Lunch Break
1:30–3:15 Generalized linear Model. Logistic regression
3:15–3:30 Snack and Refreshment Break
3:30–5:00 Hierarchical Modeling
Saturday April 28, 2018
9:00–10:45 Hierarchical Modeling
10:45–11:00 Snack and Refreshment Break
11:00–12:30 Variable selection and Model comparison
12:30–1:30 Lunch Break
1:30–3:15 Power analysis and the goals of doing research
3:15–3:30 Snack and Refreshment Break
3:30–5:00 Catch up and Individual Consultations

Instructor: Mike Kalish Ph.D.

Mike Kalish Statistics Course

Dr. Mike 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 ground-floor introduction to doing Bayesian data analysis.

Software and Computer Support

Please install software before attending the course. It is important to bring a computer to the workshops, so you can run the programs and see how their output corresponds with the presentation material.  Please install the software before arriving at the workshop. The software and programs are occasionally updated, so please check this website to be sure you have the most recent versions.

Course Prerequisites

No specific mathematical expertise is presumed. In particular, no matrix algebra is used in the course. Some previous familiarity with statistical methods such as a t-test or linear regression can be helpful, as is some previous experience with programming in any computer language, but these are not critical.

Highly Recommended Readings

Doing Bayesian Data Analysis, 2nd Edition: A Tutorial with R, JAGS, and Stan. The book is an accessible tutorial introduction to doing Bayesian data analysis. The software used in the workshop accompanies the book, and many topics in the workshop are based on the book. Reading the book before the workshop is NOT required, but reading it after will be very helpful.  Be sure to get the 2nd edition.

Course Files

Below are links to course files for those who enrolled in the course. Please download these files onto your computer on 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.

Course files and downloads will be posted on the first day of class.

Bayesian Data Analysis Statistical CourseWhy Should You Attend?

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