Bayesian Statistics and Credibility Theory

By MJ the Fellow Actuary

4.50 (88 reviews)
Udemy
platform
English
language
Math
category
instructor
Bayesian Statistics and Credibility Theory
1,731
students
1 hour
content
May 2020
last update
$29.99
regular price

What you will learn

Bayesian Statistics and Credibility Theory

Why take this course?

This short course aims to address the following syllabus objectives of the Actuarial Exams:

  1. Explain the fundamental concepts of Bayesian statistics and use these concepts to calculate Bayesian estimators.

  2. Use Bayes’ theorem to calculate simple conditional probabilities. 

  3. Explain what is meant by a prior distribution, a posterior distribution and a conjugate prior distribution.

  4. Derive the posterior distribution for a parameter in simple cases.

  5. Explain what is meant by a loss function.

  6. Use simple loss functions to derive Bayesian estimates of parameters.

  7. Explain what is meant by the credibility premium formula and describe the role played by the credibility factor.

  8. Explain the Bayesian approach to credibility theory and use it to derive credibility premiums in simple cases.

  9. Explain the empirical Bayes approach to credibility theory and use it to derive credibility premiums in simple cases.

  10. Explain the differences between the two approaches and state the assumptions underlying each of them.

Reviews

Gloria
November 20, 2021
It covers some of basic concepts in SOA exam STAM. Very helpful for understanding the concepts, which is the most important.
Salman
May 31, 2020
This course is good and balanced , would have definitely loved to see how the prior distribution was considered as Gamma, Poisson and Normal
Prekshi
May 29, 2020
The course videos were great ! Giving examples made the concept more understanding and easy and the exam style question was also really helpful ! what i feel lacked was basic in depth understanding of what Bayesian Statistic is ? why is EBCT used ? what's the purpose of EBCT etc. Overall it was nice one ! Thanks MJ !

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3149770
udemy ID
5/20/2020
course created date
7/24/2020
course indexed date
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