Applied Bayesian Data Analysis

A 2-Day Seminar Taught by Shane Jensen, Ph.D

Read recent reviews of this course


Bayesian modeling is a principled and powerful approach for the analysis of data. This seminar will develop sophisticated tools for probability modeling and data analysis from the Bayesian perspective.  We will examine Bayesian inference and prediction for simple parametric models, regression models, hierarchical models and mixture models that span a wide variety of applied data settings.  In each of these areas, we will compare and contrast the Bayesian and classical viewpoints for data analysis. We will develop a wide range of methods for model implementation, including optimization algorithms and Markov chain Monte Carlo simulation techniques. We will also examine strategies for model evaluation and validation.


COMPUTING

This seminar will use the R package for examples and exercises. R is free software, which can be downloaded at www.r-project.org. To optimally benefit from this seminar, you should bring a laptop computer running Windows or Mac OS X with R already installed.  No previous knowledge or experience with R is needed. Power outlets will be provided at each seat.


Who should attend?

Course participants will have interest in applied data analysis as well as basic knowledge of principles for statistical inference and prediction.  Participants should also have experience with basic probability topics, such as probability density functions, marginal and conditional probabilities, as well as transformation and simulation of random variables.  We will be implementing our models using the statistical software package R, though prior experience with R is not required for the course.


Materials

Participants receive a bound manual containing detailed lecture notes (with equations and graphics) and many other useful features. This book frees participants from the distracting task of note taking.


Registration and lodging

The fee of $895.00 includes all seminar materials. 

Lodging Reservation Instructions

A block of guest rooms has been reserved at the Club Quarters Hotel, 1628 Chestnut Street, Philadelphia, PA at a special rate of $142 per night. This location is about a 5 minute walk to the seminar location. In order to make reservations, call 203-905-2100 during business hours and identify yourself by using group code STA113. The room block will expire when it is full or on October 13, 2014. There will be a citywide convention in Philadelphia this weekend.  Please make your hotel reservations early as rooms will sell out.


SEMINAR OUTLINE

1. Basic Principles of Bayesian Inference (2 hours)
2. Simple Parametric Models (2 hours)
3. Regression Models (2 hours)
4. Optimization Techniques for Model Estimation (1 hour)
5. Mixture Models (2 hours)
6. Simulation Techniques for Model Estimation (1 hour)
7. Hierarchical Models (2 hours)
8. Model Validation (2 hour)


RECENT COMMENTS FROM PARTICIPANTS

“Dr. Jensen is an exceptional teacher. I would rate this as one of the best among the great courses offered by Statistical Horizons.”
  Senthil Murugan, Merck

 “This course is a good introduction to Bayesian data analysis and the focus on regression, mixture, and multilevel models had practical use for my work. The instructor was also good at explaining things in a conceptual and intuitive way, and not all statisticians are. I would highly recommend this course to anyone considering using Bayesian data analysis in their work.”
  Samara Rice, State University of New York

“I learned a lot from the course despite my not having been proficient in statistics. The instructor was excellent and the teaching was comprehensive. I highly recommend the course and the instructor.”
  Akinrinola Bankole, Guttmacher Institute 

“Shane Jensen’s mastery of this material, and his ability to translate it into accessible terms and language, is remarkable.”
  Gilda Sedgh, Guttmacher Institue

“For those who are searching for a strong introduction to Bayesian Analysis, Shane Jensen is the person you should enlist. This course was fantastic and there are so many ways to apply the techniques learned.”
  Don Hunt, Georgia State University

“I learned the material in a way that will allow me to apply it almost immediately. Dr. Jensen is a talented teacher who brings the topic to life and can clarify even the most challenging concepts.”
  Charles DiMaggio, Columbia University

“The course provides an excellent overview and includes examples with concrete, real world data. The instructor is very knowledgeable.”
  Ken Ottenbacher, University of Texas Medical Branch

“The depth of the information was fantastic, and has opened doors for future studies.”
  John Ray, Equifax

“This is a great class and really explained the fundamental concepts of Bayesian data analysis in a nontechnical format. The professor is a very good teacher and is one of the best in this business.”
  Vijay Raghavan, Forest Labs

“I found this course extremely helpful in establishing the understanding of Bayesian modeling. It’s a great refresher of the course work I did before in graduate school and much more. I’m already seeing applications in my current work. Great job!”
  Max Zhu, Equifax

“This class was well balanced between theory and application. Very understandable and a very helpful overview of Bayesian Analysis. I feel that I will be able to intelligently read papers with Bayesian analysis as well as employ this in my own work.”
  Figaro L. Loresto, Jr., University of Texas Medical Branch