Weighted Random
Weighted Random is a algorithm in the statistics family. This statistical sampling method converts weights into probabilities and samples accordingly.
Start with this mental model
Weighted Random is a algorithm in the statistics family. This statistical sampling method converts weights into probabilities and samples accordingly.
How it works, without unnecessary jargon
Weighted Random should be understood through its invariant and the state it changes at each step.
The practical complexity in this reading is Varies by implementation / input constraints; always connect that cost to the actual loops, recursion, data structure operations or input assumptions.
Read the algorithm as a sequence of decisions
Validate non-negative weights.
Build cumulative weights/probabilities.
Sample a random point and locate its interval.
Read a tiny input by hand and write down the state after every important operation. For Weighted Random, the goal is to make the invariant visible rather than to memorize code.
Varies by implementation / input constraints
Do not quote this complexity mechanically. Ask which operation or input dimension it describes, then derive the cost from the loops, recursion depth, data-structure operations and any preprocessing assumptions.
Good fit
- Use when items must not be equally likely.
What usually goes wrong
- Unnormalized/invalid weights.
- Biased random-number generation.
A professional answer shape
A strong interview explanation of Weighted Random should cover the problem it solves, its invariant, the important data structure/state, its Varies by implementation / input constraints cost, and the constraint that would make you choose a different approach.
What problem does Weighted Random solve?
Weighted Random is a algorithm in the statistics family. This statistical sampling method converts weights into probabilities and samples accordingly.
What should I mention about complexity?
Its typical cost here is Varies by implementation / input constraints, but a complete answer should connect that cost to the representation, input constraints and the dominant operations.
What is the most common mistake?
Unnormalized/invalid weights.