TMS150/MSG400, Stochastic data processing and simulation, 2018/19

This course focuses on learning how to solve stochastic problems using a selection of computer software. It consists of lectures and six mandatory projects (labs). The projects are accompanied by extensive background/introduction texts.

Latest news

Welcome to the course! The schedule for the course can be found in TimeEdit.

2019-02-04 Everything that was handed in before the re-examination deadline in January has now been graded. Grades have been reported. There will be one last chance for re-examination, see details below under "Rules for examination".

2018-11-22 Everything that was handed in before the final deadline has now been graded. Grades have been reported. Late hand-ins will be graded after the next deadline.

2018-10-30 All reports for lab 6 that were handed in before the recommended deadline have now been graded. If you handed in before the recommended deadline and have not gotten any feedback on lab 6, please contact Oskar.

2018-10-22 All reports for lab 3 that were handed in before the recommended deadline have now been graded. If you handed in before the recommended deadline and have not gotten any feedback on lab 3, please contact Rikard.

2018-10-19 All reports for lab 4 that were handed in before the recommended deadline have now been graded. If you handed in before the recommended deadline and have not gotten any feedback on lab 4, please contact Oskar.

2018-10-15 All reports for lab 2 that were handed in before the recommended deadline have now been graded. If you handed in before the recommended deadline and have not gotten any feedback on lab 2, please contact Rikard.

2018-10-15 I was contacted by collegues at University of Gothenburg who are interested in survey responses from students in science and technology. Participants are rewarded with a cinema ticket. I am not affiliated with the study and take no responsibility for it. It is in swedish only. More info here.

2018-09-28 The two remaining lectures in Palmstedtsalen have been moved to Euler. The schedule in TimeEdit is updated.

2018-09-18 Although you need to make several plots for each stock in lab 2, you do not need to include all of them in the report. Decide which plots to include based on the findings you want to convey. I clearified this in the lab PM.

2018-09-11 RStudio is now available in all computer rooms.

2018-09-07 I ordered RStudio to be installed in the computer rooms in June and checked a couple of weeks ago that it had been installed. Still, it was apparently removed from the Linux computers for some reason. The IT service says it will be installed again on Tuesday at the earliest. You can install RStudio on your own computers to get started earlier.

2018-08-31 This year 45 more students were added to this course. Unfortunately neither the teachers nor the schedulers were informed about this change until a couple of weeks before the course starts. We have been able get bigger or extra rooms. The schedule is updated. Due to this the lectures will be in many different places on the campus, so make sure you are able to find the room! Chalmers map.

Teachers

Course coordinator and lecturer: Jonatan Kallus (kallus@chalmers.se)

Computer lab teachers: Oskar Allerbo (allerbo@chalmers.se), Rikard Isaksson (rikard.isaksson@gmail.com)

Submit reports to: statdata.chalmers@analys.urkund.se

Examiner: Erik Kristiansson

Course literature

Extensive lab introductions are available on this page, under the Program section. No additional course literature. A reference book in basic mathematical statistics may be useful, for example Mathematical Statistics and Data Analysis by John A. Rice. References and tutorials for the programming languages can be found on the internet or at a library, but we expect that the examples and slides on this page should be enough to get started.

Program

None of the lectures or computer lab sessions are mandatory to attend. See section Examination for deadlines of mandatory hand-ins.


Lectures

Day Contents Lecture slides
Wed 5 Sep Introduction to the course. Lab 1: Robustness and distribution assumptions. Some examples of R: demo.R slides notes
Wed 12 Sep Recap lecture on basic statistics: point estimation, confidence intervals, hypothesis testing, p-values, maximum likelihood estimators.
Wed 19 Sep Lab 2: Decision theory. You are going to need this data set. Example of report structure: (pdf, tex source code). notes slides
Wed 26 Sep Lab 3: Reliability and survival. slides
Wed 3 Oct Lab 4: Bootstrap. Details about what your report should contain and how the points are divided between different tasks: Instructions for report writing, lab 4
Wed 10 Oct Lab 5: Monte Carlo integration. Two examples of C programs: startup1.c and startup2.c. C programming
Wed 17 Oct Lab 6: Simulation of stochastic processes.
Wed 24 Oct Extra office hours (Jonatan will be in the lecture room. You can line up and ask individual questions. There will be no lecture this day.)

Computer labs

The teachers will be available for answering questions. There will not be any formal class.

Day Main topic
Thu 6 Sep Lab 1
Mon 10 Sep Lab 1
Thu 13 Sep Lab 1
Mon 17 Sep Lab 2
Thu 20 Sep Lab 2
Mon 24 Sep Lab 2
Thu 27 Sep Lab 3
Mon 1 Oct Lab 3
Thu 4 Oct Lab 4
Mon 8 Oct Lab 4
Thu 11 Oct Lab 5
Mon 15 Oct Lab 5
Thu 18 Oct Lab 6
Mon 22 Oct Lab 6
Thu 25 Oct Lab 6

Office hours

The teachers will only be available for supervision during the scheduled lectures, computer labs and office hours. Please respect that the teachers have limited time; do not approach them with course related matters outside the scheduled times.

Primarily ask for advice during the computer labs and lectures. As a second option, mail is OK for questions that only need a short answer. If you have questions that you strongly prefer to ask outside of computer labs and lectures, you are welcome to Jonatan's office (L2120, math building) on Fridays between 13:15 and 14:00. Exception: No office hours 7 Sep, 12 Oct.

Computer labs

Computer labs are in rooms MVF22, MVF24 and MVF25 in the physics building at campus Johanneberg. To use the computers, you will need a Chalmers computer account. Visit the IT helpdesk if you don't have one already link.

The lecture room MVF23 is also booked for computer labs (except Mondays 10-12). Sit there if you work on your own laptop.


Oskar and Rikard will be available during the computer labs.

If the teachers are busy, write your name on the blackboard in the big room with linux computers and they will come to you. If you are in another room, also write which room. 10-12 the rooms will be more crowded and the wait for help will be longer. Be there at 8 for less waiting time and more space!

To open Rstudio on the linux computers, open a terminal and type rstudio. To open Matlab on the linux computers, open a terminal and type matlab.

Writing: I big part of your work in this course will be spent on writing reports. Being able to express knowledge and results clearly and concisely is an important skill for all scientists and engineers. This skill is one of the learning goals of this course. Advice on Latex and report writing is given here. Examples of report outline can be found under Program. This will help you in report writing, but it is not necessary to use the example structure.

Working on your own computer: You can, of course, work on your own computer. You should be able to download Matlab via Chalmers IT when you have a Chalmers computer account. R is free and can be downloaded from this site, and you can download RStudio from here. You can use Sharelatex or Overleaf for preparing latex reports. Register with your Chalmers email at Sharelatex to get a premium account. There also are free Latex compilers out there, for example this one. For compiling C code you can install gcc on Linux and Mac. For Windows there are other C compilers.

Course requirements

The requirements and learning goals of the course can be found in the course plan.

Examination

The course is examined by completing the mandatory assignments. There is no written exam.

Deadlines and instructions for handing in

Exercise Language Type of examination Recommended deadline Final deadline
Lab 1 - Robustness and distribution assumptions Matlab and R Only answers Thu 13 Sep, 11:45 Fri 2 Nov, 23:59
Lab 2 - Decision theory Matlab Complete report Fri 28 Sep, 23:59 Fri 2 Nov, 23:59
Lab 3 - Reliability and survival R Complete report Fri 5 Oct, 23:59 Fri 2 Nov, 23:59
Lab 4 - Bootstrap R Complete report Fri 12 Oct, 23:59 Fri 2 Nov, 23:59
Lab 5 - Monte Carlo integration C Only answers Thu 18 Oct, 11:45 Fri 2 Nov, 23:59
Lab 6 - Simulation of stochastic processes. R Complete report Fri 26 Oct, 23:59 Fri 2 Nov, 23:59

Lab 1 and 5:
Only results and answers to the questions are needed (the questions are written in bold font in the lab pm). You can pass the exercise either by showing your results and answering the questions to a teacher at the exercise session (in the computer room), or by sending the answers by email in pdf format to statdata.chalmers@analys.urkund.se. If you hand in by email, the pdf should contain your figures and/or tables, brief texts answering the questions and the code you wrote to solve the lab.

Lab 2, 3, 4 and 6:
Complete reports are needed. Each student needs to write individual reports. The report should be written in Latex and not exceed 10 pages, including figures, but excluding appendix. Send the reports by email in pdf format to statdata.chalmers@analys.urkund.se. The reports will be checked for plagiarism. If you hand in the report before the recommended deadline stated above, you will get the corrections back before the final deadline. In this way you will be able to hand in a return with corrections if you want. You can hand in each report maximum two times (original + return).
Instructions for hand-in of returns: Add a new section in the beginning of your report that informs the reader exactly where the report has been improved. (If your report becomes longer than 10 pages just because of this new section, it is OK). Send in the return from the same mail address as you used for the original, so that we can easily find the original and our comments.


Rules for examination


Cheating

You are encouraged to solve the assignments in pairs, but reports should be written individually. All hand-ins will be checked for plagiarism. It is, for example, not accepted to write the report together and then change words or sentences to make them different. Cheating will be reported to the disciplinary committee of Chalmers/GU.


Number of points given for lab reports

Project Maximum Pass
Lab 2 13 7
Lab 3 11 5
Lab 4 14 6
Lab 6 10 4
Total 48 22

For a given project report, 0.5 points will be deducted if the report is not clearly structured or is otherwise hard to understand. Likewise, 0.5 points will be deducted if the code attached to the report is not properly structured and commented.


Grading

For all grades below you need to have a pass on each of the labs (see table above), it is not enough to only have the total number of points according to the limits below.


Chalmers:
GradeRequirement
3Pass all 6 labs
4Pass all 6 labs and have 32 points in total
5Pass all 6 labs and have 42 points in total

GU:
GradeRequirement
GPass all 6 labs
VGPass all 6 labs and have 37 points in total

Course evaluation

The course evaluation consists of a mid-course meeting for the teachers and the student representatives, a course evaluation survey sent to all students and a course evaluation meeting for the teachers and student representatives after the course.

Notable changes since last year: