EH144-7-SU-CO:
Introduction to Applied Bayesian Statistics
2021/22
Essex Summer School in Social Science Data Analysis
Colchester Campus
Summer
Postgraduate: Level 7
Current
Monday 25 April 2022
Friday 01 July 2022
30
31 March 2021
Requisites for this module
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This course introduces the basic theoretical and applied principles of Bayesian statistical analysis.
The Bayesian paradigm is particularly well-suited for the types of data that social scientists encounter given its recognition of the mobility of population parameters, its ability to incorporate information from prior research, and its ability to update estimates as new data are observed.
The course begins with a discussion of the strengths and weaknesses of the Bayesian approach and the philosophical differences between the Bayesian and frequentist approaches. Most of the course content will focus on estimating and interpreting a variety of models (linear, dichotomous and polytomous choice, poisson, missing data, latent variable, and multilevel) from an applied Bayesian perspective.
No information available.
On successful completion of the module, students will:
1. Have an advanced understanding of Bayesian statistical analysis, sufficient for producing publishable work in the social sciences
2. Have experience and skills Bayesian statistical analysis in a range of practical applications
3. Be able to apply their knowledge of Bayesian statistical analysis to their own project
Participants are expected to be well-versed in the linear model and proficient in maximum likelihood models and probability theory. Additionally, participants should have some basic understanding of derivative calculus and matrix algebra and some familiarity with R.
The following learning and teaching methods will inform the pedagogic process of the course:
The lectures will introduce the key theoretical concepts of the social science research methods. Foundations of the social science methods and instructions on how to apply them for solving research problems will be established through the lectures (Learning outcome 1 and 3). The lectures will also equip students with a critical understanding of the strengths and weakness of the specific techniques, directing them to the most appropriate approach for various contexts.
The labs will focus on practical applications of the methods taught in the lectures. For each topic, relevant applications will be practiced in the lab sessions, which will help students to acquire the skills for implementing the methods and solving problems (Learning outcome 2 and 3).
This module does not appear to have a published bibliography for this year.
Assessment items, weightings and deadlines
| Coursework / exam |
Description |
Deadline |
Coursework weighting |
| Coursework |
Assesment two |
|
50% |
| Coursework |
Assessment one |
|
50% |
Exam format definitions
- Remote, open book: Your exam will take place remotely via an online learning platform. You may refer to any physical or electronic materials during the exam.
- In-person, open book: Your exam will take place on campus under invigilation. You may refer to any physical materials such as paper study notes or a textbook during the exam. Electronic devices may not be used in the exam.
- In-person, open book (restricted): The exam will take place on campus under invigilation. You may refer only to specific physical materials such as a named textbook during the exam. Permitted materials will be specified by your department. Electronic devices may not be used in the exam.
- In-person, closed book: The exam will take place on campus under invigilation. You may not refer to any physical materials or electronic devices during the exam. There may be times when a paper dictionary,
for example, may be permitted in an otherwise closed book exam. Any exceptions will be specified by your department.
Your department will provide further guidance before your exams.
Overall assessment
Reassessment
Module supervisor and teaching staff
No
No
No
Available via Moodle
No lecture recording information available for this module.
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