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Bayesian Statistics Course

October 1 @ 1:00 pm - October 4 @ 5:00 pm CDT

| $945.00 – $1,295.00

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LIVESTREAM and/or ASYNCHRONOUS – 4-day Bayesian Data Analysis Statistics Course

Course Overview:

Unlock the Power of Bayesian Statistics: Join Our Intensive Five-Day Bayesian Course

Are you ready to take your statistical skills to the next level? Look no further than our comprehensive five-day Bayesian statistics course. As one of our most popular training workshops each year, this course offers advanced students, faculty, and researchers from all disciplines a unique opportunity to dive deep into Bayesian data analysis with expert instructors Mauricio Garnier-Villarreal, Ph.D. and Esteban Montenegro-Montenegro, Ph.D.

Propel Your Career Forward:
– Academic Research: Publish groundbreaking studies and contribute to cutting-edge research in your field.
– Market Research: Gain insights into consumer behavior, market trends, and product performance.
– Healthcare: Analyze patient outcomes, healthcare delivery systems, and treatment effectiveness.
– Public Policy: Address societal challenges through evidence-based recommendations.
– Environmental Science: Develop strategies for sustainability and conservation efforts.

Skills Gained:
– Advanced understanding of Bayesian statistics principles
– Proficiency in Bayesian data analysis techniques
– Ability to apply Bayesian methods to complex research problems
– Skills in model estimation, validation, and interpretation
– Expertise in handling and interpreting Bayesian output
– Competence in using statistical software for Bayesian analysis
– Familiarity with advanced topics such as hierarchical modeling and Markov chain Monte Carlo (MCMC) methods

Seminar Topics:

  • 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.

Bayesian Statistics Seminar Description:

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.

Instructors: Mauricio Garnier Villarreal, Ph.D.

Researcher from Costa Rica, working at the Sociology department from the Vrije Universiteit Amsterdam (VU). Overall my work is about development and test of data analysis methods, and their applications in a variety of applied fields.

I am passionate about open source, and collaborative research.

Esteban Montenegro Montenegro, Ph.D.

I’m an Assistant Professor at the Psychology and Child Development Department. I focus my research ideas in problems related to quantitative psychology. I’m always doing research on latent variable modeling. I applied my passion for statistics in topics related to healthy aging and dementia.

 

Bayesian Statistics Course Includes:

Materials, downloads, recorded course video viewable for up to one year.

Learning Objectives:

After engaging in course lectures and discussions as well as completing the hands-on practice activities with real data, participants will be able to:

  • Describe the fundamental properties of Bayesian reasoning.
  • Implement Bayes’ rule
  • Utilize Markov Chain Monte Carlo in the brms package for R to evaluate Bayesian models.
  • Apply the appropriate prior distribution to a Bayesian analysis.
  • Conduct generalized linear models using Bayesian estimation, including simple linear regression, multiple regression, and robust regression.
  • Conduct a T-test using Bayesian estimation.
  • Conduct a Bayesian ANOVA.
  • Evaluate Logistic and Count regression models using Bayesian estimation.
  • Evaluate Multilevel linear regression models using Bayesian estimation.
  • Conduct a Bayesian Multilevel growth curve analysis.
  • Understand how issues of missing data are addressed in the Bayesian framework.
  • Explain the difference between frequentist and Bayesian statistics
  • Critically evaluate applications of Bayesian analysis in scientific studies.
  • Analyze data using Bayesian techniques in R.
  • Specify, estimate, evaluate, and compare different Bayesian models to fit possible hypothesis.

Participants are encouraged to bring data of interest to work with during the week, there will be time to work on it with help of the instructor. Work on datasets of interest will magnify the learning and impact of the course.

Participants will also complete the course with a foundation for future learning about Bayesian statistics and knowledge about available resources to guide such endeavors.

Bayesian Data Analysis Solutions Prerequisites:

Required:

  • Basic proficiency in multiple linear regression, the generalized linear model.
  • Intermediate proficiency with at least one statistical software package (e.g., SPSS, Stata, SAS, R, etc.).

Not required but advantageous:

  • At least limited experience (e.g., graduate-level course) with multilevel models
  • Intermediate proficiency in R, or syntax base software

No level of proficiency beyond basic awareness is assumed for skills related to:

  • Bayesian Statistics
  • Data analysis using R

Bayesian Statistics Software and Computer Support:

It is important to bring a notebook computer to the seminar, so you can run the programs and see how their output corresponds with the presentation material.  Please install the software before arriving at the seminar. We’ll be estimating the examples in R language using the packages rstan and brms. The latter will be the main package in this course. You can read more about brms package: https://mc-stan.org/users/interfaces/brms.

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.

Bayesian Statistics Course Files

Below are links to seminar files for those who enrolled in the seminar. Please download these files onto your computer before the first day of the seminar. 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.

Instructor will provide password on first day of seminar:
Click Here to Access Bayesian Data Analysis Seminar Files

All statistical software used at Stats Camp will be available, free to participants, on our SMORS (statistical modeling on remote servers) system for the duration of camp.

Stats Camp 2024: Bayesian Data Analysis
Tuesday October 1, 2024
9:00–10:45 Brief introduction to R and Rstudio
10:45–11:00 Rest Break
11:00–12:30 Know your distributions. Application of priors
12:30–1:30 Rest Break
1:30–3:15 General linear model. Multiple regression, and robust regression
3:15–3:30 Rest Break
3:30–5:00 General linear model. Robust regression
Wednesday October 2, 2024
9:00–10:45 Model comparison
10:45–11:00 Rest Break
11:00–12:30 General linear model. T-test and ANOVA
12:30–1:30 Rest Break
1:30–3:15 Regularization and polynomials in linear regression
3:15–3:30 Rest Break
3:30–5:00 Generalized linear Model. Logistic regression
Thursday October 3, 2024
9:00–10:45 Generalized linear Model: Count regression
10:45–11:00 Rest Break
11:00–12:30 Multilevel linear regression
12:30–1:30 Rest Break
1:30–3:15 Multilevel linear regression
3:15–3:30 Rest Break
3:30–5:00 Multilevel growth curve/repeated measures
Friday October 4, 2024
9:00–10:45 Introduction to missing data
10:45–11:00 Rest Break
11:00–12:30 Missing data in Bayesian inference
12:30–1:30 Rest Break
1:30~3:30 Individual Consultations

Download Sample Slides and Preview Course Video

Please fill out and submit the form below to get instant access to sample course materials.

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