Singapore University of Social Sciences

Fundamentals of Probability

Fundamentals of Probability (MTH210)

Applications Open: 01 October 2024

Applications Close: 15 November 2024

Next Available Intake: January 2025

Course Types: Modular Undergraduate Course

Language: English

Duration: 6 months

Fees: $1392 View More Details on Fees

Area of Interest: Science and Technology

Schemes: Alumni Continuing Education (ACE)

Funding: To be confirmed

School/Department: School of Science and Technology


Synopsis

Probability brings a sense of logic to a world characterised by randomness and uncertainty. MTH210 Fundamentals of Probability gives an elementary introduction to probability theory for students with knowledge of elementary calculus. This course will cover not only the probability theory but will engage with various examples to illustrate the wide scope of applications of probability. Upon completion of the course, students will possess the essential knowledge of probability theory, equipping them to apply probability models adeptly in addressing real-world practical challenges.

Level: 2
Credit Units: 5
Presentation Pattern: EVERY REGULAR SEMESTER

Topics

  • Probability axioms
  • Conditional probability
  • Random variables
  • Probability mass functions for discrete random variables
  • Probability density functions for continuous random variables
  • Cumulative distribution functions
  • Expectation and variance of random variables
  • Distributions of discrete random variables
  • Distributions of continuous random variables
  • Normal distribution
  • Normal approximation to approximate Binomial distribution
  • Poisson approximation to approximate Binomial distribution

Learning Outcome

  • Calculate the probability of the occurrence of an event.
  • Compute conditional probability and identify independent events.
  • Determine expectation and variance of random variables.
  • Solve probability distributions.
  • Comment on results of Normal/Poisson approximation to Binomial distribution.
  • Apply probability models in practical settings.
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