Undergraduate Course: Probability (MATH08066)
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 8 (Year 2 Undergraduate) |
Credits | 10 |
| Home subject area | Mathematics |
Other subject area | None |
| Course website |
None |
Taught in Gaelic? | No |
| Course description | Students taking this course should have either passed both 'Introduction to Linear Algebra' and 'Calculus and its Applications' or be taking 'Accelerated Algebra and Calculus for Direct Entry' :
A beginning probability course, with no pre-requisites. |
Information for Visiting Students
| Pre-requisites | None |
| Displayed in Visiting Students Prospectus? | No |
Course Delivery Information
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| Delivery period: 2012/13 Semester 1, Available to all students (SV1)
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Learn enabled: Yes |
Quota: None |
| Location |
Activity |
Description |
Weeks |
Monday |
Tuesday |
Wednesday |
Thursday |
Friday |
| King's Buildings | Lecture | Lecture Theatre A, JCMB | 1-11 | | 11:10 - 12:00 | | | | | King's Buildings | Lecture | Lecture Theatre B JCMB | 1-11 | | | | | 11:10 - 12:00 | | King's Buildings | Tutorial | JCMB, rooms TBC | 1-11 | | | | 15:00 - 15:50or 16:10 - 17:00 | or 09:00 - 09:50or 10:00 - 10:50 |
| First Class |
Week 1, Tuesday, 11:10 - 12:00, Zone: King's Buildings. Lecture Theatre A, JCMB |
| No Exam Information |
Summary of Intended Learning Outcomes
1. To understand the basic notions of Probability
2. To understand conditional probability and independence.
3. To be familiar with the geometric, binomial and Poisson discrete probability densities.
4. To be familiar with the uniform, negative exponential and Normal distributions.
5. To be able to work with some random variables, and calculate their expected values.
6. To be familiar with a 2-state discrete-time Markov chain. |
Assessment Information
| Up to 15% Continuous Assessment, the remainder examination. |
Special Arrangements
| None |
Additional Information
| Academic description |
Not entered |
| Syllabus |
Week 1: Introduction, foundations of Probability: sample spaces and events (Chap. 1 ¿ 2.2 of Scheaffer and Young.)
Week 2: Definition of Probability, counting rules in Probability. (Ch. 2.3-2.6 of SY)
Week 3: Conditional Probability, independence, total Probability, Bayes¿ Rule (Ch 3.1-3.3 of SY.)
Week 4: Discrete random variables, expectation, variance,
Bernoulli and binomial distributions. (4.1-4.4)
Week 5: Geometric and Negative Binomial distributions, Poisson distribution, Probability generating functions. (4.4¿4.7,4.10)
Week 6:, Finite-state discrete-time Markov chains. (4.11)
Week 7: Continuous random variables and their probability distributions and expectations, Uniform distribution, Exponential distribution, Normal distribution. (5.1¿5.4,5.6)
Week 8: Reliability and redundancy, bivariate probability distributions. (5.9, 6.1)
Week 9: Convergence in Probability, Weak Law of Large Numbers, convergence in Distribution, Central Limit Theorem. (8.1-8.4).
Week 10: Poisson process, connection with Poisson distribution. (9.1)
Week 11: Overview, catchup, revise.
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| Transferable skills |
Not entered |
| Reading list |
Not entered |
| Study Abroad |
Not entered |
| Study Pattern |
Not entered |
| Keywords | Prob |
Contacts
| Course organiser | Dr Tibor Antal
Tel: (0131 6)51 7672
Email: Tibor.Antal@ed.ac.uk |
Course secretary | Mr Martin Delaney
Tel: (0131 6)50 6427
Email: Martin.Delaney@ed.ac.uk |
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© Copyright 2012 The University of Edinburgh - 31 August 2012 4:18 am
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