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Markov's Inequality - Intuitively and visually explained
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Classroom Contents
Probability for Actuarial Science
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- 1 Probability: Lesson 1- Basics of Set Theory
- 2 Probability: Lesson 2 - Sample Space, Events and Compound Events
- 3 Probability Lesson 3 - Basics of Probability Theory/ Kolmogorov Axioms
- 4 Inclusion Exclusion Principle, DeMorgan's Law Examples
- 5 Probability Lesson 4 Part 1: Counting Techniques
- 6 Probability Lesson 4 part 2 Counting Techniques
- 7 Probability Lesson 5: Conditional Probability and Multiplication Law of Probability
- 8 Probability Lesson 6: Independent Events
- 9 Lesson 7 Law of Total Probability
- 10 Lesson 8: Bayes rule
- 11 Bayes rule Example
- 12 Lesson 9 :Random Variables - Introduction
- 13 Discrete Random Variables
- 14 Lesson 11 Continuous Random Variables
- 15 Lesson 12 The Expectation of Random Variables
- 16 Lesson 13: Variance of a Random Variable
- 17 Lesson 14: Properties of Expectation and Variance
- 18 Lesson 15: Moment Generating Functions
- 19 Lesson 16 Bernoulli and Binomial Distribution Part 1
- 20 Lesson 16 Binomial Distribution Part 2
- 21 Lesson 17: Geometric Distribution Part 1
- 22 Lesson 17: Geometric Distribution part II
- 23 Lesson 18: Negative Binomial Distribution - Part 1
- 24 Lesson 18: Negative Binomial distribution Part II
- 25 Lesson 19 Hypergeometric Distribution - Introduction
- 26 Poisson Distribution
- 27 Exponential Distribution
- 28 Poisson Process and Gamma Distribution
- 29 Gamma Distribution
- 30 Univariate transformation of a random variable
- 31 Uniform Distribution
- 32 Normal Distribution
- 33 Beta Distribution
- 34 Chi Squared Distribution
- 35 Markov's Inequality - Intuitively and visually explained
- 36 Proof of Markov's Inequality
- 37 Chebyshev’s Inequality
- 38 Introduction to Multivariate Probability Distributions
- 39 Joint Probability Distribution Of Discrete Random Variables
- 40 Joint Probability Mass Function Example
- 41 Probability Density Function Explained
