General Prerequisites:
Part A Probability.
Course Term: Hilary
Course Lecture Information: 16 lectures[Teaching responsibility of the Department of Statistics. Please note, this course is offered from the schedule of Mathematics Department Units]The course materials have been made available through the Stats department on Canvas, including the lecture videos: https://canvas.ox.ac.uk/courses/116819
Course Weight: 1
Course Level: H
Assessment Type: Written Examination
Course Overview:
This course is intended to show the power and range of probability by considering real examples in which probabilistic modelling is inescapable and useful. Theory will be developed as required to deal with the examples.
Learning Outcomes:
Course Synopsis:
Poisson processes and birth processes. Continuous-time Markov chains. Transition rates, jump chains and holding times. Forward and backward equations. Class structure, hitting times and absorption probabilities. Recurrence and transience. Invariant distributions and limiting behaviour. Time reversal. Renewal theory. Limit theorems: strong law of large numbers, strong law and central limit theorem of renewal theory, elementary renewal theorem, renewal theorem, key renewal theorem. Excess life, inspection paradox.
Applications in areas such as: queues and queueing networks - M/M/s queue, Erlang's formula, queues in tandem and networks of queues, M/G/1 and G/M/1 queues; insurance ruin models; stochastic epidemic models.
Applications in areas such as: queues and queueing networks - M/M/s queue, Erlang's formula, queues in tandem and networks of queues, M/G/1 and G/M/1 queues; insurance ruin models; stochastic epidemic models.