Oregon Health Sciences University (OHSU) and Portland State University (PSU) School of Public Health

Skip to Main Content

Course Directory and Schedules

Course Schedules

Course Schedules by Term

Fall 
Fall 2022 GR Courses
Planning Schedule

Winter
Winter 2023 GR Courses
Planning Schedule

(rev. 7.5.22)

 

Spring
Spring 2023 GR Courses
Planning Schedule
(rev. 7.5.22)

Interprofessional Education Course Schedule

Interprofessional education occurs when students from two or more professions learn about, from, and with each other to enhance collaboration and improve health outcomes. At least 1 credit of interprofessional education is required by all MPH degree programs.

Most courses with OHSU subject code IPE (Inter-Professional Education) or UNI (University Curriculum) satisfy the interprofessional education requirement. Other courses may also serve; consult your advisor. For a list of IPE and UNI courses, descriptions, and their intended schedule, download this spreadsheet.This list is subject to change — contact the course instructor if you would like to enroll.

SPH Course Descriptions

Descriptions of all School of Public Health courses can also be found in the course catalog of the most recent edition of the PSU Bulletin.

  • The results are being filtered by the organization: Bayesian Methods for Data Analysis - BSTA 521

Bayesian Methods for Data Analysis – BSTA 521

Course Information

The methods students learned in the biostatistical applied and theoretical sequences were based on the “frequentist” method of statistical reasoning, where probability is understood to be the long-run frequency of a ‘repeatable’ event, and statistics that are computed are based on a specific study only. Bayesian methods are based on a different philosophy – that probability of an event is based on ALL information known at the time. Bayesian methods for data analysis enable one to combine information from previous similar and independent studies (prior information), with information from a new study, yielding updated inference for model parameters. This course will cover the concept of Bayesian analysis, posterior distribution, Bayesian inference and prediction, prior determination, one parameter and two parameter models, Bayesian hierarchical models, Bayesian computation, model criticism and selection as well as basic comparison of Bayesian and Frequentist Inferences. Real life examples in medical and health science will be used to explain the concept and application of Bayesian models.

 

Prerequisites

BSTA 511/611 Estimation and Hypothesis Testing for Applied Biostatistics (passing grade of "B" or better); BSTA 512/612 Linear Models; and BSTA 550 Introduction to Probability (passing grade of "B" or better).

Course Code

BSTA 521

Credit

3