## Every Class Registration Provides the Following:

- Live lecture offering the teaching and reviewing of exam syllabus topics, reinforcing the concepts by working through practice problems, and illustrating how concepts are tested in P/1 problems
- Open forum immediately following live lecture to discuss any questions with instructor
- Problem set/solutions distributed to students following lecture to reinforce concepts learned
- Q & A correspondence with instructor
- Online office hours
- Recording of lecture sent to students

### Schedule

## Live Classes

Class 1: Sat, 2/6

12:00 PM – 3:00 PM EST

12:00 PM – 3:00 PM EST

**Class 1: Bayes Theorem**

- Prior Distributions. What are they and examples
- The Likelihood Distribution. Why is it useful and examples
- The Posterior Distribution. How is it used and interpretation
- The Formula and setting up the problem.

Class 2: Sat, 2/13

12:00 PM – 3:00 PM EST

12:00 PM – 3:00 PM EST

**Class 2: Loss Functions**

- Defining the terminology of Loss Problems
- Calculating Loss with Deductibles
- Calculating Loss with Limits of Insurance Payments

Class 3: Sat, 2/13

4:00 PM – 6:00 PM EST

4:00 PM – 6:00 PM EST

**Class 3: Moment Generating Functions**

- Definition of a MGF
- Examples from a density function
- Properties of MGF
- The MGF for Sums of density functions
- Expected Value using MGF
- Variance using MGF

Class 4: Sat, 2/20

12:00 PM – 4:00 PM EST

12:00 PM – 4:00 PM EST

**Class 4: Negative Binomial, Geometric, Binomial, Hypergeometric, Multinomial**

- Definition of each
- How to choose the correct distribution
- Examples of each distribution

Class 5: Sun, 2/27

12:00 PM – 3:00 PM EST

12:00 PM – 3:00 PM EST

**Class 5: Multivariate and Multivariate Conditional**

- Examples of Multivariate Functions
- Finding Expectations
- Finding Marginal Distributions
- Finding Conditional Probability
- Finding Conditional Expectation

Class 6: Sat, 2/27

12:00 PM – 3:00 PM EST

12:00 PM – 3:00 PM EST

**Class 6: Variance and Covariance**

- Univariate Variance Definition
- Examples of Univariate Variance for Discrete Variables
- Examples of Univariate Variance for Continuous Variables
- Covariance of several Discrete and Continuous Variables
- E(x^2) – (E(x)}^2 = V(x)

Applications

Class 7: Sun, 3/7

12:00 PM – 3:00 PM EST

12:00 PM – 3:00 PM EST

**Class 7: Special Distributions**

- Exponential Distribution

- The Distribution and its components

- The Expected Value and Variance

- Survivorship Applications The Compliment

- The Exponential and Poisson Interaction

- Inter-arrival Times - Beta Distribution

- The Distribution and it components

- The Expected Value and Variance

- The Tau Function

- Applications - Gamma Distribution

- The Distribution and its components

- The Expected Value and Variance

- Sums of Exponential Distributions

- Applicating ons

Class 8: Sun, 3/7

5:00 PM – 7:00 PM EST

5:00 PM – 7:00 PM EST

**Class 8: Mixed Distributions**

- Definition of a Mixed Distribution
- Examples for Probability Computation
- Examples for Expectation and Variance

## Join The Exam P Syllabus Refresher Series Program and strengthen your foundation of the concepts that will lead to you passing your exam.

REGISTER