Stochastic Processes II

Synopsis

MTH362 Stochastic Processes II will complement MTH361 Stochastic Processes I by extending the study of Markov chains and getting into Poisson Processes. The two courses will be useful for applications to finance, data science and engineering. Additionally, computer simulation with Python will also be taught.

Level: 3
Credit Units: 5
Presentation Pattern: EVERY JULY

Topics

Mean Time Spent
Branching Processes
Time Reversible Markov Chains
Markov Chain Monte Carlo Methods
Markov Decision Processes
Hidden Markov Chains
Predicting the States
Exit Distributions
Exit Times
Exponential Distribution
Compound Poisson Processes
Transformations

Learning Outcome

Compute probabilities of events of a Markov chain expectation or distribution of random variables.
Show the validity of given mathematical statements in stochastic processes.
Calculate the mean and variance of random variables.
Determine the time it takes for a Markov chain to reach absorbing state.
Solve for the asymptotic behaviour of Markov chains.
Formulate Markov chain models from word problems.

Part-time Programmes / CET Courses

Singapore University of Social Sciences

Stochastic Processes II

Stochastic Processes II (MTH362)

Synopsis

Topics

Learning Outcome