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Graduate courses

Departments' graduate courses for PhD-students.

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Syllabus for

Academic year
MVE550 - Stochastic processes and Bayesian inference  
Stokastiska processer och Bayesiansk inferens
 
Syllabus adopted 2019-02-22 by Head of Programme (or corresponding)
Owner: MPENM
7,5 Credits
Grading: TH - Five, Four, Three, Fail
Education cycle: Second-cycle
Major subject: Mathematics
Department: 11 - MATHEMATICAL SCIENCES


Teaching language: English
Application code: 20141
Open for exchange students: Yes

Module   Credit distribution   Examination dates
Sp1 Sp2 Sp3 Sp4 Summer course No Sp
0118 Examination 6,0 c Grading: TH   6,0 c   18 Jan 2020 pm H   08 Apr 2020 am DIST   17 Aug 2020 am J
0218 Written and oral assignments 1,5 c Grading: UG   1,5 c    

In programs

MPDSC DATA SCIENCE AND AI, MSC PROGR, Year 1 (compulsory)
TKITE SOFTWARE ENGINEERING, Year 3 (elective)
TKTFY ENGINEERING PHYSICS, Year 3 (elective)
TKTEM ENGINEERING MATHEMATICS, Year 2 (compulsory)

Examiner:

Petter Mostad

  Go to Course Homepage


Eligibility:


In order to be eligible for a second cycle course the applicant needs to fulfil the general and specific entry requirements of the programme that owns the course. (If the second cycle course is owned by a first cycle programme, second cycle entry requirements apply.)
Exemption from the eligibility requirement: Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling these requirements.

Course specific prerequisites

A basic course in mathematical statistics

Aim

Building on a first course in mathematical statistics, this course is meant to give knowledge about a larger inventory of stochastic models, in particular stochastic processes, and greater knowledge about Bayesian inference, in particular in the context of these models. Together, this knowledge should give a solid basis for practical application and prediction using stochastic processes in connection with data analysis, and for further studies in statistics and probability theory.

Learning outcomes (after completion of the course the student should be able to)

After completion of the course the student should be able to use certain basic stochastic processes as models for real phenomena, and adapt these models using observed data. The student should be able to make predictions from the models, both using theoretical properties and using computer based simulation. The student should be able to make computer based inference using MCMC for certain simple models, and in general understand and apply the Bayesian inferential paradigm.

Content

Discrete time Markov chains. Branching processes. Basic principles for Bayesian inference, using discretization and conjugate priors. Hidden Markov models (HMM). Monte Carlo integration and Markov chain Monte Carlo (MCMC) simulation. Poisson processes. Time-continuous Markov chains. Introduction to Brownian motion.



Organisation

Lectures and excercise classes. Obligatory assignments.

Literature

Dobrow: Introduction to Stochastic Processes with R. (Available at Chalmers as e-book). Wiley 2016.

Lecture Notes.

Examination including compulsory elements

Written exam. Obligatory assignments.


Page manager Published: Thu 04 Feb 2021.