Advanced Statistics MSc
Stochastic Processes (Level M) STATS5026
- Academic Session: 2024-25
- School: School of Mathematics and Statistics
- Credits: 10
- Level: Level 5 (SCQF level 11)
- Typically Offered: Semester 1
- Available to Visiting Students: Yes
- Collaborative Online International Learning: No
Short Description
To provide a good understanding of the key concepts of stochastic processes in various settings: discrete time and finite state space; discrete time and countable state space; continuous time and countable state space.
Timetable
Two lectures per week for 10 weeks and fortnightly tutorials.
Excluded Courses
STATS4024 Stochastic Processes
Assessment
120-minute, end-of-course examination (100%)
Main Assessment In: April/May
Course Aims
To provide a good understanding of the key concepts of stochastic processes in various settings: discrete time and finite state space; discrete time and countable state space; continuous time and countable state space.
Intended Learning Outcomes of Course
By the end of this course students should be able to:
■ Explain the concept of a homogeneous Markov chain;
■ Define what it means for a matrix to be stochastic and discuss the consequences this has for the eigenvalues;
■ Explain what it means for a state to be absorbing, periodic, persistent, transient, or ergodic;
■ Explain the difference between a stationary and a limiting distribution;
■ Decide whether a Markov chain has a unique limiting distribution;
■ Describe the gambler's ruin problem in terms of a discrete-time Markov chain;
■ Calculate the probability of ruin and the expected duration of a game in the gambler's ruin problem;
■ Explain the difference between a homogeneous and an inhomogeneous linear difference equation and the standard procedure to solve them;
■ Derive the probability distribution of a random walk;
■ Calculate the first return of a symmetric random walk;
■ Define the concept of a homogeneous Poisson process, and derive the form of the distribution of the inter-arrival times;
■ Calculate the expected length and waiting time for a queue in which arrivals form a Poisson process;
■ Define the concepts of a reliability function and a hazard function;
■ Calculate the expected number of renewals in a renewal process;
■ Explain the difference between a discrete-time and a continuous-time Markov chain and explain the concept of a rate matrix;
■ Decide whether a birth-death process has a stationary distribution;
■ Read further into one topic related to the course and answer questions on this topic.
Minimum Requirement for Award of Credits
None