Applied Bayesian Data Analysis
A 2-Day Seminar Taught by Shane Jensen, Ph.D.
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.
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.
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 STA327. For guaranteed rate and availability, you must reserve your room no later than February 27, 2014.
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)
“I started off knowing very little about Bayesian statistics and now feel I have a solid background for both evaluating published articles that use these methods and for doing my own analysis.”
Richard Amdur, George Washington University
“Very good overview of the topic. Just the right amount of information to get started without getting into too much details of the math. Very good mixture of theory and examples. Teacher is excellent! Makes it a fun experience while learning.”
Xiaoyan Chen, Avon
“The course teaches Bayesian data analysis from theory to practical code. Very useful. The speaker is eager to answer questions and explain abstruse ideas very clearly.”
Jingbo Niu, Boston University
“I’ve come away with a much better idea of what Bayesian statistics is about, it’s different from what I thought! I’m not sure yet how I would actually use it in my research, but if nothing else I feel like it has given me a new perspective and understanding of statistical methods in general.”
Matissa Hollister, Dartmouth College
“Jensen gave a very good presentation on this topic. This course starts with an introduction to Bayesian theory, explains the differences between Bayesian and frequentist approaches, and then moves onto advanced topics: how to use Bayesian theory to perform data analysis, such as different sampling techniques to estimate the posterior distribution. I liked the pace and materials from the course. It gave me an overall picture of Bayesian techniques and enabled me to check more references in order to learn by myself on this topic”
Qin Zhang, INC Research LLC
“Outstanding course. A nice mix of step-by-step logic behind a Bayesian approach, statistical theory, and applied examples.”
Doug Wiebe, University of Pennsylvania