# Stochastic Processes, Markov Chains and Markov Jumps

Udemy Course Stochastic Processes, Markov Chains and Markov Jumps | NED

by Michael Jordan

Section 1

*A***Markov****chain**is a**stochastic**model describing a sequence of possible events in which the probability of each event depends only on the state attained in the previous event. In continuous-time, it is known as a**Markov****process**. It is named after the Russian mathematician Andrey**Markov**.by Michael Jordan

What you'll learn

- The basics of Stochastic Processes and Markov Chains

Requirements

- An understanding of actuarial statistics is required

Description

**In this course we look at Stochastic Processes, Markov Chains and Markov Jumps**

**We then work through an impossible exam question that caused the low pass rate in the 2019 sitting.**

**This question requires you to have R Studio installed on your computer.**

**Things we cover in this course:**

Section 1

- Stochastic Process
- Stationary Property
- Markov Property
- White Noise
- Increments
- Random Walks

- Markov Chains
- Transition Probabilities
- Chapman-Kolmogorov Equations
- Transition Matrix
- Stationary Probability Distributions
- Irreducibility
- Periodicity

- R Studio Exam Question

- Markov Jump Process
- Transition and Survival Probabilities
- Kolmogorov's Forward Differential Equation
- Transition Rates
- Generator Matrix
- Kolmogorov's Backward Differential Equation

Who this course is for:

- Actuarial Students writing the professional exams

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