IN PERSON – 5-day Bayesian Data Analysis Statistics Course
This is a comprehensive five day Bayesian statistics course. This is our most popular training event each year where students and professionals have the opportunity to learn directly from a vetted statistics instructor. The live lectures and one-on-one Q & A provide a rare chance to bring your own personal research and let us help you break through any roadblocks standing in the way of progressing your work. The intended audience is advanced students, faculty, and other researchers, from all disciplines, who want a ground-floor introduction to doing Bayesian data analysis statistics.
- 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 for Bayesian analysis.
- An extensive array of applications, including comparison of two groups, ANOVA-like designs, linear regression, logistic regression, multilevel regression, and growth models, count regression, robust regression, regularization and polynomials, and missing data treatments in Bayesian inference.
Many fields of science are transitioning from null hypothesis significance testing (NHST) to Bayesian Data Analysis Solutions. 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 seminar shows you how to do Bayesian data analysis, hands on.
Instructor: Mauricio Garnier-Villarreal, Ph.D.
Mauricio is a full time assistant professor at Vrije Universiteit Amsterdam. His Ph.D focused on Quantitative Psychology at the University of Kansas completed in the summer of 2016. His research focus is on the application of Bayesian methods to complex structure data for longitudinal analysis, from both mixed-effects and SEM models. He has experience not only working in the test and development of methods, but also in the application of these in data; in different fields like special education, cognitive decline in aging, healthy aging… (orcid.org/0000-0002-2951-6647). He has been involved in the Stats Camp since 2011.
Instructor: Esteban Montenegro, Ph.D.
I’m a researcher at the UC Davis Alzheimer’s Disease Center- East Bay. I conduct data analysis using advanced statistical methods such as latent variable modeling. I have 6 years of experience programming in R, and I love learning about Linux and statistical new tools.…I’m always open to new projects and ideas, I collaborate with several teams around the world on topics related to healthy aging, Alzheimer Disease and other health related topics.
APA Continuing Education Credits:
This course offers 29 hours of Continuing Education Credits. Stats Camp Foundation is approved by the American Psychological Association to sponsor continuing education for psychologists. Stats Camp Foundation maintains responsibility for this program and its content.
Bayesian Statistics Course Includes:
Materials, downloads, recorded course video viewable for up to one year.