29/08/21

Cards (or card-like game data structures) bring extra information. With dice we only cared about the values, with cards we also introduce other categories (e.g. suits).

When we calculate the probabilities of card-like game data structures, we must also include the extra information in our counting.

Video game hypothetical example

Reward system for the end of a level

• Imagine the reward system as a deck of cards (reward deck)
• Each card in the deck has some reward on it
• Whilst there are many duplicate values as rewards, each card should be a unique reward
• Each time we draw a card from the reward deck, the care should be removed from it forever (on a die, this would be like removing a side of it each time a suit was rolled so that it isn't an option anymore)

Hyper-Geometric Distribution Formula

Hyper-Geometric Distribution Formula = Non-replacing probability within a given range

We can find these within the given range = Hyper

Astronomically change = Geometric

Probabilities where they land = Distribution

This formula has 4 variables:

Population size (N) e.g. number of cars in our deck

Sample size (n) e.g. how many cards we draw from the population size

Number of possible successes (K) e.g. if we want an ace it would be how many aces there are in the deck

Number of success we're looking for (k) if we wat to know the probability of drawing one of those aces = k-1