Event Phone: 1-610-715-0115

Cancellation Policy: If you cancel your registration at least two weeks before the course is scheduled to begin, you are entitled to a full refund (minus a processing fee of $50).

In the unlikely event that Statistical Horizons LLC must cancel a seminar, we will do our best to inform you as soon as possible of the cancellation. You would then have the option of receiving a full refund of the seminar fee or a credit towards another seminar. In no event shall Statistical Horizons LLC be liable for any incidental or consequential damages that you may incur because of the cancellation.

A 2-Day Seminar Taught by Jeff Gill, Ph.D.

Bayesian methods have revolutionized statistics over the last quarter of a century. This is not an exaggeration. The appeal of Bayesian statistics is its intuitive basis in making direct probability statements for all assertions, and the ability to blend disparate types of data into the same model. Bayesian models take existing knowledge and update it as new data becomes available. This principle works across all scientific disciplines. The cost of this added inferential power is more reliance on computing. Fortunately, there are powerful software packages for Bayesian statistics that are free and easy to use (with some training).

This introductory course covers the theoretical and applied foundations of basic Bayesian statistical analysis with an emphasis on computational tools for Bayesian hierarchical models. We will discuss model checking, model assessment, and model comparison. The course will cover Bayesian stochastic simulation (Markov chain Monte Carlo) in depth. We will fit linear and nonlinear specifications with multiple levels, longitudinal features, and non-normal distributional assumptions. Lectures will include theoretical discussions of modeling and estimation as well as practical guidance for fitting Bayesian multilevel models with software. Applications will be drawn from the social and biomedical sciences.

This is an applied course, but it covers the statistical theory necessary for understanding the methods, and therefore includes discussion of required mathematical statistics for Bayesian inference. There will be hands-on activities throughout the two days so that participants will leave knowing actually how to apply Bayesian methods to their own data.