course-details-portlet

MA8702 - Advanced Modern Statistical Methods

About

Examination arrangement

Examination arrangement: Portfolio assessment
Grade: Passed/Failed

Evaluation Weighting Duration Grade deviation Examination aids
Arbeider 30/100
Muntlig eksamen 70/100

Course content

This subject is normally taught every second year, next
time spring 2016. A condition is that sufficiently many students are registered.
The course will give a theoretical and methodological
introduction and discussion of modern statistical methods, but assumes also good computational skills. Topics to be discussed are a selection of the following; theory and methods for Markov chain Monte Carlo, Hidden Markov chains, Gaussian Markov random fields, mixtures, non-parametric methods and regression, splines, bootstrapping, classification and graphical models, latent Gaussian models and their approximate Bayesian inference. Relative weighting of the various topics will vary according to need.

Learning outcome

1. Knowledge.
The course will give a theoretical and methodological introduction and discussion of modern statistical methods, but assumes also good computational skills. Topics to be discussed are a selection of the following; theory and methods for Markov chain Monte Carlo, Hidden Markov chains, Gaussian Markov random fields, mixtures, non-parametric methods and regression, splines, bootstrapping, classification and graphical models, latent Gaussian models and their approximate Bayesian inference.

2. Skills.
The students should learn and be able to use the basic techniques in the modern theoretical statistics. In particular, Markov chain, Monte Carlo, Hidden Markov chains, Gaussian Markov random fields, mixtures, non-parametric methods and regression, splines, bootstrapping, classification and graphical models, latent Gaussian models and their approximate Bayesian inference.


3. Competence.
The students should be able to participate in scientific discussions and conduct researches in statistics on high international level. They should be able to participate in applied projects involving statistical methods and apply their knowledge in problems in theoretical statistics.

Learning methods and activities

Lectures, alternatively guided self-study.

Compulsory assignments

  • Exercises

Specific conditions

Compulsory activities from previous semester may be approved by the department.

Course materials

Will be announced at the start of the course.

More on the course
Facts

Version: 1
Credits:  7.5 SP
Study level: Doctoral degree level

Coursework

Term no.: 1
Teaching semester:  SPRING 2016

Language of instruction: -

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Subject area(s)
  • Statistics
Contact information

Department with academic responsibility
Department of Mathematical Sciences

Examination

Examination arrangement: Portfolio assessment

Term Status code Evaluation Weighting Examination aids Date Time Examination system Room *
Autumn ORD Arbeider 30/100
Room Building Number of candidates
Autumn ORD Muntlig eksamen 70/100
Room Building Number of candidates
Spring ORD Arbeider 30/100
Room Building Number of candidates
Spring ORD Muntlig eksamen 70/100
Room Building Number of candidates
  • * The location (room) for a written examination is published 3 days before examination date. If more than one room is listed, you will find your room at Studentweb.
Examination

For more information regarding registration for examination and examination procedures, see "Innsida - Exams"

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