Introduction to Bayesian Statistics

Introduction to Bayesian Statistics

This course will teach you the basic ideas of Bayesian Statistics: how to perform Bayesian analysis for a binomial proportion, a normal mean, the difference between normal means, the difference between proportions, and for a simple linear regression model.

This course will teach you the basic ideas of Bayesian Statistics: how to perform Bayesian analysis for a binomial proportion, a normal mean, the difference between normal means, the difference between proportions, and for a simple linear regression model.

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Overview

This course will introduce you to the basic ideas of Bayesian Statistics. In Bayesian statistics, population parameters are considered random variables having probability distributions. These probabilities measure “degree of belief”. The rules of probability (Bayes’ theorem) are used to revise our belief, given the observed data. Bayesian methods will be contrasted with the comparable frequentist methods, demonstrating the advantages this approach offers. Bayes’ theorem gives the general way to find the predictive distribution of future observations. There is no such general method in frequentist statistics, only a collection of methods that sometimes work.

Learning Outcomes

Students who complete this course will gain a solid foundation in how to apply and understand Bayesian statistics, and how to understand Bayesian methods vs. frequentist methods.  Topics covered include: an introduction to Bayesian concepts; Bayesian inference for binomial proportions, Poisson means, and normal means; modeling including simple linear regression and more.

  • Perform Bayesian analysis for a binomial proportion and a normal mean
  • Perform Bayesian analysis for differences in proportions and means
  • Perform Bayesian analysis for a simple linear regression
  • Contrast Bayesian methods with frequentist methods

Who Should Take This Course

Biostatisticians, those designing and analyzing clinical trials, social science statisticians, environmental and geophysical scientists; nearly all fields of statistical analysis are amenable to a Bayesian approach.

Instructors

dr-william-m-bolstad

Dr. William M. Bolstad

Dr. William M. Bolstad is a Professor at the University of Waikato, New Zealand, Dept. of Statistics, and has 30 years of teaching experience. Dr. Bolstad is the author of Introduction to Bayesian Statistics, 2nd Edition (the course text), and has pioneered the use of Bayesian methods in teaching the first year statistics course.
See Instructor Bio

Course Syllabus

Week 1

Introduction to Bayesian Statistics

  • Logic probability & uncertainty
  • Discrete random variables
  • Bayesian inference for discrete random variables

Week 2

Bayesian Inference for Binomial Proportion and Poisson Mean

  • Continuous random variables
  • Bayesian inference for binomial proportion
  • Comparing Bayesian and frequentist inferences for proportion
  • Bayesian inference on Poisson mean

Week 3

Bayesian Inference for Normal Mean

  • Bayesian inference for normal mean
  • Comparing Bayesian and Frequentist inferences for mean
  • Bayesian inference for difference between means

Week 4

Modeling

  • Bayesian Inference for Simple Linear Regression Model
  • Robust Bayesian methods
  • Bayesian inference for normal standard deviation

Class Dates

2020

Jan 17, 2020 to Feb 14, 2020

Jul 3, 2020 to Jul 31, 2020

2021

No classes scheduled at this time.

2022

No classes scheduled at this time.

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Prerequisites

Introductory Statistics

We assume you are versed in statistics or have the equivalent understanding of topics covered in our Statistics 1 and Statistics 2 courses. but do not require them as eligibility to enroll in this course. Please review the course description for each of our introductory statistics courses, estimate which best matches your level of understanding of the material covered in these courses, then take the short assessment test for that course. If you can not answer more than half of the questions correctly, we suggest you take our Statistics 1 and Statistics 2 courses prior to taking this course.

Familiarity with calculus is required.

What Our Students Say​

I would recommend this class to anybody (with a basis in probability theory and calculus) who wishes to get acquainted with bayesianism.

Jimmy Bourque
Exam Developer at Royal College of Physicians and Surgeons of Canada

I learned a great deal from this course. I thought that the instructor, Dr. Congdon, prepared excellent lessons for the course.  Dr. Congdon's responses to the questions on the discussion board were clear and very helpful. The TA for this course was also excellent.

Margaret Palmisano

Frequently Asked Questions

Can I transfer or withdraw from a course?

We have a flexible transfer and withdrawal policy that recognizes circumstances may arise to prevent you from taking a course as planned. You may transfer or withdraw from a course under certain conditions.

  • Students are entitled to a full refund if a course they are registered for is canceled.
  • You can transfer your tuition to another course at any time prior to the course start date or the drop date, however a transfer is not permitted after the drop date.
  • Withdrawals on or after the first day of class are entitled to a percentage refund of tuition.

Please see this page for more information.

Who are the instructors at the Institute?

The Institute has more than 60 instructors who are recruited based on their expertise in various areas in statistics. Our faculty members are:

  • Authors of well-regarded texts in their area;
  • Advisory board members;
  • Senior faculty; and
  • Educators who have made important contributions to the field of statistics or online education in statistics.

The majority of our instructors have more than five years of teaching experience online at the Institute.

Please visit our faculty page for more information on each instructor at The Institute for Statistics Education.

Please see our knowledge center for more information.

What type of courses does the Institute offer?

The Institute offers approximately 80 courses each year. Topics include basic survey courses for novices, a full sequence of introductory statistics courses, bridge courses to more advanced topics. Our courses cover a range of topics including biostatistics, research statistics, data mining, business analytics, survey statistics, and environmental statistics.

Please see our course search or knowledge center for more information.

Do your courses have for-credit options?

Our courses have several for-credit options:

  • Continuing education units (CEU)
  • College credit through The American Council on Education (ACE CREDIT)
  • Course credits that are transferable to the INFORMS Certified Analytics Professional (CAP®)

Please see our knowledge center for more information.

Is the Institute for Statistics Education certified?

The Institute for Statistics Education is certified to operate by the State Council of Higher Education for Virginia (SCHEV). For more information visit: http://www.schev.edu

Please see our knowledge center for more information.

Visit our knowledge base and learn more.

FAQs + Knowledge Base

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Additional Course Information

Organization of Course

This course takes place online at The Institute for 4 weeks. During each course week, you participate at times of your own choosing – there are no set times when you must be online. Course participants will be given access to a private discussion board. In class discussions led by the instructor, you can post questions, seek clarification, and interact with your fellow students and the instructor.

At the beginning of each week, you receive the relevant material, in addition to answers to exercises from the previous session. During the week, you are expected to go over the course materials, work through exercises, and submit answers. Discussion among participants is encouraged. The instructor will provide answers and comments, and at the end of the week, you will receive individual feedback on your homework answers.

Time Requirements

This is a 4-week course requiring 10-15 hours per week of review and study, at times of your choosing.

Homework

Homework in this course consists of short answer questions to test concepts and guided data analysis problems using software.

In addition to assigned readings, this course also has supplemental readings available online, and an end of course data modeling project.

Course Text

The required text for this course is Introduction to Bayesian Statistics, 3rd edition, by W. M. Bolstad.

Please order a copy of your course textbook prior to course start date.

Software

The instructor will offer illustrations in Minitab and R, and exercises can be done using these two packages.

Software Uses and Descriptions | Available Free Versions
To learn more about the software used in this course, or how to obtain free versions of software used in our courses, please read our knowledge base article “What software is used in courses?” 

Course Fee & Information

Enrollment
Courses may fill up at any time and registrations are processed in the order in which they are received. Your registration will be confirmed for the first available course date unless you specify otherwise.

Transfers and Withdrawals
We have flexible policies to transfer to another course or withdraw if necessary.

Group Rates
Contact us to get information on group rates.

Discounts
Academic affiliation?  In most courses you are eligible for a discount at checkout.

New to Statistics.com?  Click here for a special introductory discount code.  

Invoice or Purchase Order
Add $50 service fee if you require a prior invoice, or if you need to submit a purchase order or voucher, pay by wire transfer or EFT, or refund and reprocess a prior payment.

Options for Credit and Recognition

This course is eligible for the following credit and recognition options:

No Credit
You may take this course without pursuing credit or a record of completion.

Mastery or Certificate Program Credit
If you are enrolled in mastery or certificate program that requires demonstration of proficiency in this subject, your course work may be assessed for a grade.

CEUs and Proof of Completion
If you require a “Record of Course Completion” along with professional development credit in the form of Continuing Education Units (CEU’s), upon successfully completing the course, CEU’s and a record of course completion will be issued by The Institute upon your request.

INFORMS-CAP
This course is recognized by the Institute for Operations Research and the Management Sciences (INFORMS) as helpful preparation for the Certified Analytics Professional (CAP®) exam and can help CAP® analysts accrue Professional Development Units to maintain their certification.

Supplemental Information

There is no supplemental content for this course.

Miscellaneous

There is no additional information for this course.

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Introduction to Bayesian Statistics
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