Unit 13 of 13
Integrated Probability Investigations
- 2 lessons
- Videos 83 to 84
Two investigations bring the course together. Each builds a complete model from stated assumptions, calculates exact answers, checks them and says what the model cannot show.
The first follows an inspection system through a joint table, Bayes’ theorem, a binomial count of alerts and conditional expected review times, then tests how the answers change when one input changes. The second combines parcel categories, Poisson arrivals, exponential waits and a two-state Markov chain for a scanner, multiplies probabilities only where independence was assumed and compares an exact batch probability with a recorded simulation.
Every lesson will be one video. The videos are coming soon, and each lesson page already has the definitions and an example for every key term.
Complete Model Investigations
A Discrete Probability Investigation
How to specify an inspection model, build its joint table from conditional rates, count alerts with a binomial law, find conditional expected work, test assumption sensitivity and compare predictions with simulated output.
Video 83
A Complete Introduction to Probability Investigation
How to combine parcel categories, Poisson arrivals, exponential waits and a two-state scanner chain into one model, multiply only where independence was assumed, test sensitivity and check one submodel by simulation.
Video 84