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MATH3251: STOCHASTIC PROCESSES III

Please ensure you check the module availability box for each module outline, as not all modules will run in each academic year. Each module description relates to the year indicated in the module availability box, and this may change from year to year, due to, for example: changing staff expertise, disciplinary developments, the requirements of external bodies and partners, and student feedback. Current modules are subject to change in light of the ongoing disruption caused by Covid-19.

Type Open
Level 3
Credits 20
Availability Available in 2023/24
Module Cap
Location Durham
Department Mathematical Sciences

Prerequisites

  • Analysis in Many Variables II (MATH2031) AND (Markov Chains II (MATH2707) ORProbability II (MATH2647))

Corequisites

  • None.

Excluded Combinations of Modules

Aims

  • To develop models for processes evolving randomly in time, and probabilistic methods for their analysis, building on the treatment of probability at Levels 1 and 2. Students completing this course should be equipped to independently study much of the vast literature on applications of stochastic processes to problems in physics, engineering, chemistry, biology, medicine, psychology, and other fields.

Content

  • Conditional expectation
  • Branching processes
  • Coupling theory
  • Discrete renewal theory
  • Discrete-time martingales and their applications
  • Continuous time Markov chains
  • Poisson processes
  • Continuous time martingales

Learning Outcomes

Subject-specific Knowledge:

  • By the end of the module students will be able to:
  • Rigorously define branching processes, couplings of random variables/stochastic processes, renewal processes, martingales and Markov chains in discrete and continuous time.
  • Illustrate the above processes with examples.
  • Explain the key theorems covered in the course (that govern the behaviour of these processes) and apply them to: calculate/estimate probabilities of events; calculate/estimate expectations of observed quantities; quantify similarity/difference between two stochastic processes; classify processes according to long term behaviour.
  • (Re)construct proofs of main theorems .
  • Apply theorems to solve unseen problems (appropriate to Level III and of similar type to those seen in the course) concerning stochastic processes.
  • Model "real-world" or informally described systems that evolve in time subject to randomness, using appropriate stochastic processes.

Subject-specific Skills:

  • In addition students will have enhanced mathematicalskills in the following areas: modelling, computation.

Key Skills:

  • Students will have basic mathematical skills in the following areas: problem solving, modelling, computation.

Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module

  • Lectures demonstrate what is required to be learned and theapplication of the theory to practical examples.
  • Problems classes show how to solve example problems in an ideal way, revealing also the thought processes behind such solutions.
  • Formative assessments provide feedback to guide students in the correct development of their knowledge and skills in preparation for summative assessment.
  • The end-of-year examination assesses the knowledge acquiredand the ability to solve predictable and unpredictableproblems.

Teaching Methods and Learning Hours

ActivityNumberFrequencyDurationTotalMonitored
Lectures422 per week in Michaelmas and Epiphany; 2 in Easter 1 Hour42 
Problems Classes8Fortnightly in Michaelmas and Epiphany1 Hour8 
Preparation and Reading150 
Total200 

Summative Assessment

Component: ExaminationComponent Weighting: 100%
ElementLength / DurationElement WeightingResit Opportunity
Written examination3 Hours100 

Formative Assessment

Four assignments in each of the first two terms.

More information

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