ST11512 Introduction to Probability
Introductory description
The module runs in Term 2 and provides elementary introduction to the theory of probability. The topics include axioms of probability, combinatorics, independent events, conditional probability, random variables, discrete and continuous probability distributions, expectation and variance, joint probability distributions, independence of random variables, sum of independent random variables, covariance and correlation.
This module is core for students with their home department in Statistics and is not available to students from other departments. Students from other departments should consider ST111 Probability A and ST112 Probability B instead.
Module aims
To lay the foundation for all subsequent modules in probability and statistics, by introducing the key notions of mathematical probability and developing the techniques for working with probability distributions and random variables.
Outline syllabus
This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.
 Experiments with random outcomes: the notions of random experiment, sample space and events. Operations with sets and their interpretation.
 Axioms of probability. Properties of a probability measure: Boole’s inequality, continuity of a probability measure, inclusionexclusion formula.
 Finite sample spaces with equally likely outcomes.
 Independence of events. Conditional probabilities. Bayes theorem.
 The notion of a random variable. Examples in both discrete and continuous settings. Indicator random variables.
 The notion of the distribution of a random variable. Probability mass functions and density functions. Cumulative distribution functions.
 Expectation of random variables. Properties of expectation.
 Mean and variance of distributions. Chebyshev's inequality.
 Independence of random variables. Joint distributions. Covariance and correlation. CauchySchwartz inequality.
 Addition of independent random variables: convolutions. Moment generating function and use to compute convolutions.
 Important families of distributions: Binomial, Poisson, negative Binomial, exponential, Gamma and Gaussian. Their properties, genesis and interrelationships.
Learning outcomes
By the end of the module, students should be able to:
 Understand key notions of mathematical probability including random variables and their distributions
 Appreciate the role of randomness in mathematical modelling of real world situations.
 Use appropriate mathematical techniques to calculate the probabilities of events, and the expectations of random variables
Indicative reading list
Ross, A first course in probability, Prentice Hall, 1994
Pitman, Probability, Springer texts in Statistics
Suhov and Kelbert, Probability and Statistics by Example: Basic Probability and Statistics.
View reading list on Talis Aspire
Subject specific skills
Mathematical, analytical, problem solving
Transferable skills
Analytical, problem solving, investigative skills, communication, good working habits.
Study time
Type  Required  Optional 

Lectures  30 sessions of 1 hour (25%)  2 sessions of 1 hour 
Seminars  8 sessions of 1 hour (7%)  
Tutorials  5 sessions of 1 hour (4%)  
Private study  53 hours (44%)  
Assessment  24 hours (20%)  
Total  120 hours 
Private study description
Weekly revision of lecture notes and materials, wider reading and practice exercises, working on problem sets and preparing for examination.
Costs
No further costs have been identified for this module.
You do not need to pass all assessment components to pass the module.
Students can register for this module without taking any assessment.
Assessment group D1
Weighting  Study time  

Written assignment  10%  12 hours 
The assignment will contain a number of questions for which solutions and / or written responses will be required. The preparation and completion time noted below refers to the amount of time in hours that a wellprepared student who has attended lectures and carried out an appropriate amount of independent study on the material could expect to spend on this assignment. You will write your answers on paper and submit as instructed. 

Multiple Choice Quizzes  10%  12 hours 
A number of multiple choice quizzes which will take place during the term that the module is delivered. 

Online Examination  80%  
The examination paper will contain four questions, of which the best marks of THREE questions will be used to calculate your grade. ~Platforms  Moodle 
Assessment group R
Weighting  Study time  

Online Examination  Resit  100%  
The examination paper will contain four questions, of which the best marks of THREE questions will be used to calculate your grade. ~Platforms  Moodle 
Feedback on assessment
Answers to problems sets will be marked and returned to students in a tutorial or seminar taking place the following week when students will have the opportunity to discuss it.
Solutions and cohort level feedback will be provided for the examination.
Courses
This module is Core for:

USTAG302 Undergraduate Data Science
 Year 1 of G302 Data Science
 Year 1 of G302 Data Science
 Year 1 of USTAG304 Undergraduate Data Science (MSci)
 Year 1 of USTAG300 Undergraduate Master of Mathematics,Operational Research,Statistics and Economics
 Year 1 of USTAG1G3 Undergraduate Mathematics and Statistics (BSc MMathStat)

USTAGG14 Undergraduate Mathematics and Statistics (BSc)
 Year 1 of GG14 Mathematics and Statistics
 Year 1 of GG14 Mathematics and Statistics

USTAY602 Undergraduate Mathematics,Operational Research,Statistics and Economics
 Year 1 of Y602 Mathematics,Operational Research,Stats,Economics
 Year 1 of Y602 Mathematics,Operational Research,Stats,Economics