Advanced Probability
| Site: | Young Education |
| Course: | Probability and Combinatorics |
| Book: | Advanced Probability |
| Printed by: | Guest user |
| Date: | Friday, 25 September 2026, 1:16 AM |
1. Bayes' Theorem
Learning outcomes
- I can explain Bayes' Theorem.
- I can calculate posterior probabilities.
- I can interpret Bayesian reasoning.
- I can solve diagnostic testing problems.
- I can apply Bayesian thinking to real-world situations.
2. Independent and Dependent Events
Learning outcomes
- I can distinguish independent and dependent events.
- I can calculate joint probabilities.
- I can analyse probability models.
- I can solve complex probability problems.
- I can justify probability calculations.
3. Markov Processes
Learning outcomes
- I can describe simple Markov processes.
- I can interpret transition diagrams.
- I can calculate transition probabilities.
- I can model simple stochastic systems.
- I can apply Markov models to practical situations.
4. Simulation and Monte Carlo Methods
Learning outcomes
- I can explain simulation techniques.
- I can perform Monte Carlo simulations.
- I can estimate probabilities experimentally.
- I can compare theoretical and simulated results.
- I can evaluate simulation accuracy.
5. Probability Models
Learning outcomes
- I can construct probability models.
- I can evaluate model assumptions.
- I can compare competing models.
- I can solve applied probability problems.
- I can communicate probability reasoning effectively.