Undergraduate Course: Essentials in Analysis and Probability (MATH10047)
Course Outline
| School |
School of Mathematics |
College |
College of Science and Engineering |
| Course type |
Standard |
Availability |
Available to all students |
| Credit level (Normal year taken) |
SCQF Level 10 (Year 4 Undergraduate) |
Credits |
10 |
| Home subject area |
Mathematics |
Other subject area |
Specialist Mathematics & Statistics (Honours) |
| Course website |
https://info.maths.ed.ac.uk/teaching.html
|
Taught in Gaelic? |
No |
| Course description |
Random events, sigma-algebras, monotone classes.
Measurable spaces, random variables - measurable functions.
Measures, probability measures, signed measures.
Borel sets in R^d, Lebesgue measure.
Sequences of events and random variables, Borel-Cantelli lemma.
Distributions of random variables. Independence of random variables.
Integral of measurable functions - mathematical expectation,.
Moments of random variables, L_p spaces.
Convergence concepts of measurable functions.
Limit theorems for integrals.
Week and strong laws of large numbers.
Completeness of L_p spaces.
Conditional expectation and conditional distribution of random variables.
Fubini's theorem. |
Information for Visiting Students
| Pre-requisites |
None |
| Displayed in Visiting Students Prospectus? |
Yes |
Course Delivery Information
|
| Delivery period: 2010/11 Semester 1, Available to all students (SV1)
|
WebCT enabled: Yes |
Quota: None |
| Location |
Activity |
Description |
Weeks |
Monday |
Tuesday |
Wednesday |
Thursday |
Friday |
| King's Buildings | Lecture | | 1-11 | | 10:00 - 10:50 | | | | | King's Buildings | Lecture | | 1-11 | | | | | 10:00 - 10:50 |
| First Class |
Week 1, Tuesday, 10:00 - 10:50, Zone: King's Buildings. JCMB, room 6301 |
| Exam Information |
| Exam Diet |
Paper Name |
Hours:Minutes |
Stationery Requirements |
Comments |
| Main Exam Diet S2 (April/May) | | 2:00 | 16 sides. No YAF | |
Summary of Intended Learning Outcomes
1. To provide the students with the basic notions and results from measure theory and integration, motivating them by fundamental concepts of probability theory.
2. To prepare a firm ground for further studies in analysis, in modern probability theory and in their applications. |
Assessment Information
Examination only.
|
Special Arrangements
| None |
Additional Information
| Academic description |
Not entered |
| Syllabus |
Not entered |
| Transferable skills |
Not entered |
| Reading list |
Not entered |
| Study Abroad |
Not entered |
| Study Pattern |
Not entered |
| Keywords |
EAP |
Contacts
| Course organiser |
Dr Tom Mackay
Tel: (0131 6)50 5058
Email: T.Mackay@ed.ac.uk |
Course secretary |
Mrs Alison Fairgrieve
Tel: (0131 6)50 6427
Email: Alison.Fairgrieve@ed.ac.uk |
|
copyright 2011 The University of Edinburgh -
31 January 2011 7:59 am
|
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