Applications of stochastic models to real-world processes observed over time and space. Topics include Poisson processes, renewal processes, Markov chains, hidden Markov models, geostatistics, spatial point processes, model fitting, forecasting and simulation.
|Paper title||Stochastic Modelling|
|Teaching period||First Semester|
|Domestic Tuition Fees (NZD)||$886.35|
|International Tuition Fees (NZD)||$3,766.35|
- STAT 261 or STAT 270
- Schedule C
- Arts and Music, Science
- Learning Outcomes
The aim of this paper is to introduce students to many of the statistical learning techniques that are now used to analyse high-dimensional data. Students will learn the underlying rationale for each method and gain practice in using it on real data in R.
On successful completion of the paper, students will be able to:
- Apply an important class of modern temporal and spatial stochastic models to real data.
- Describe the assumptions underlying use of each of these methods.
- Determine an appropriate type of stochastic models for a given analysis.
- Describe probabilistic forecast using stochastic processes.
- Critically appraise research literature in terms of the statistical methods used.
- Use a standard statistical programming language (R) to analyse data and simulate stochastic processes.
- Teaching staff
To be confirmed
To be advised
- Graduate Attributes Emphasised
- Global perspective, Interdisciplinary perspective, Lifelong learning, Scholarship,
Communication, Critical thinking, Environmental literacy, Information literacy, Research,
View more information about Otago's graduate attributes.