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Stochastic Processes, Markov Chains and Markov Jumps

 Stochastic Processes, Markov Chains and Markov Jumps
Udemy Course Stochastic Processes, Markov Chains and Markov Jumps | NED
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
Section 2
  • Markov Chains
  • Transition Probabilities
  • Chapman-Kolmogorov Equations
  • Transition Matrix
  • Stationary Probability Distributions
  • Irreducibility
  • Periodicity
Section3
  • R Studio Exam Question
Section 4
  • 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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