Course pages 2012–13

**Subsections**

##

Paper 2: Probability

This course is not taken by NST or PPST students.

*Lecturer: Dr R.J. Gibbens*

*No. of lectures:* 8

*Suggested hours of supervisions:* 3

*Prerequisite course: Discrete Mathematics I*

*This course is a prerequisite for the Part IB course Mathematical Methods for Computer Science, and the following Part II courses: Artificial Intelligence II, Computer Systems Modelling, Information Theory and Coding, Computer Vision, Digital Signal Processing, Natural Language Processing and Information Retrieval.*

### Aims

The main aim of this course is to provide a foundation in Probability with emphasis on areas that are particularly applicable to Computer Science.

### Lectures

**Review of elementary probability theory.**Random variables. Discrete and continuous distributions. Means and variances, moments, independence, conditional probabilities. Bayes’s theorem. [2 lectures]**Probability generating functions.**Definitions and properties. Use in calculating moments of random variables and for finding the distribution of sums of independent random variables. [2 lectures]**Multivariate distributions and independence.**Random vectors and independence. Joint and marginal density functions. Variance, covariance and correlation. Conditional density functions. [2 lectures]**Elementary stochastic processes.**Random walks. Recurrence and transience. The Gambler’s Ruin problem. Solution using difference equations. [2 lectures]

### Objectives

At the end of the course students should

- have a thorough understanding of concepts in probability theory and a practical knowledge of associated calculations;
- be aware of applications of probability across the field of computer science.

### Recommended reading

* Grimmett, G. & Welsh, D. (1986). *Probability: an introduction*. Oxford University Press.