Stochastic Processes

Learning Outcomes

Learning Outcomes

Having successfully completed this module you will be able to:

• Determine the stationary and equilibrium distributions of a Markov chain
• State the Kolmogorov equations for a Markov process where the transition intensities depend not only on age/time, but also on the duration of stay in one or more states
• Describe a Markov chain and its transition matrix
• Define and explain the basic properties of Brownian motion, demonstrate an understanding of stochastic differential equations and then to integrate, and apply the Ito formula
• Demonstrate how a Markov chain can be simulated
• Recall the definition and derive some basic properties of a Poisson process
• Solve the Kolmogorov equations in simple cases
• Classify a stochastic process according to whether it operates in continuous or discrete time and whether it has a continuous or a discrete state space, and give examples of each type of process
• Demonstrate how a Markov jump process can be simulated
• Calculate the distribution of a Markov chain at a given time
• Derive the Kolmogorov equations for a Markov process with time independent and time/age dependent transition intensities
• Describe a time-inhomogeneous Markov chain and its simple applications
• Understand, in general terms, the principles of stochastic modelling
• Understand the definition of a stochastic process and in particular a Markov process, a counting process and a random walk
• Understand survival, sickness and marriage models in terms of Markov processes
• Classify the states of a Markov chain as transient, null, recurrent, positive recurrent, periodic, aperiodic and Ergodic

Summative

This is how we’ll formally assess what you have learned in this module.

Referral

This is how we’ll assess you if you don’t meet the criteria to pass this module.

Repeat

An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.