Adding Weighted Dice with a Visual Convolution

A grid and its diagonals show the probability distribution of a sum.

GSGrant Sanderson진행자
채널
3Blue1Brown
게시일
2023년 12월 12일
영상
1:00
읽기
1분
원문
영어
YouTube에서 열기
스크롤하면 영상이 오른쪽 아래로 작아져 계속 재생됩니다. 자막은 읽기 언어를 따릅니다.
읽기 언어2개 언어로 읽을 수 있음
이 글은 아직 한국어 번역이 없어 영어 원문으로 표시합니다.
요약

30초 요약

This 3Blue1Brown short shows how to calculate the sum of two independent weighted dice by multiplying outcome probabilities and adding along diagonals: a discrete convolution.

  1. 1GridA grid lists all outcome pairs.
  2. 2Pair probabilityFor independent dice, multiply face probabilities.
  3. 3Equal sumsEqual sums lie on diagonals.
  4. 4Add diagonalsAdd pair probabilities on each diagonal.
  5. 5ConvolutionThe resulting distribution is a convolution.

핵심 정리

  1. A grid lists all outcome pairs.
  2. For independent dice, multiply face probabilities.
  3. Equal sums lie on diagonals.
  4. Add pair probabilities on each diagonal.
  5. The resulting distribution is a convolution.
전문

챕터별로 다듬은 전문 읽기

원본 자막만 보기
01

Weighted dice

List and weight outcome pairs.

Suppose I roll two weighted dice and want the probability of each possible sum. This short illustrates the computation for independent rolls; the full video linked in its description extends the idea to continuous distributions.

I start with a six-by-six grid whose cells represent all pairs of outcomes. The dice need not be fair, so each face can have its own probability.

For independent dice, the probability of a pair is the probability of the first face multiplied by the probability of the second. If the rolls are dependent, a joint distribution is needed instead.

02

Diagonal sums

Combine pairs with equal totals.

The probabilities can be shown as heights in a three-dimensional plot. Pairs with the same sum lie on the same diagonal of the grid.

For example, one diagonal collects outcomes summing to six and another collects outcomes summing to seven. Adding the heights along each diagonal gives the probability of that sum.

Collapsing the plot along the diagonals produces the full distribution of the sum. This operation is discrete convolution of the two independent dice distributions.

같은 채널의 다른 글

슬라이드 20장40:44EN
3Blue1Brown· 2026년 10월 16일Grant Sanderson: There's never been a better time to go into mathGrant Sanderson, Constantin Kogler · 3Blue1BrownStudents fear AI has solved mathematics. Grant Sanderson argues the opposite: even if machines prove theorems better than we do, the institution of math stays valuable—and this is a rare moment when newcomers can reshape it.MathematicsAI for scienceHuman-AI Collaboration+2
슬라이드 29장51:55EN
3Blue1Brown· 2026년 9월 18일The last IMO problem AI could not solveGrant Sanderson · 3Blue1BrownGrant Sanderson walks through IMO 2025 Problem 6 — a tiling puzzle that frontier models missed — and the visual patience it takes to prove a construction is optimal.MathematicsAI researchReasoning & test-time compute+2
0:55EN
3Blue1Brown· 2026년 7월 24일The 64 Sugar Cubes Puzzle: Six Equal DistancesA 4×4×4 block has three colors; prove that six same-colored pairs share a distance.MathematicsEducation
33:51EN
3Blue1Brown· 2026년 7월 16일What Cross-Entropy Connects: Compression, Language Models and KL DivergenceGrant Sanderson · 3Blue1BrownGrant Sanderson builds cross-entropy from code lengths and uses it to explain next-token training and distillation.MathematicsMachine LearningFoundation Models+1
슬라이드2:25EN
3Blue1Brown· 2026년 6월 12일How Shannon Measured the Entropy of EnglishA guessing game helped Claude Shannon estimate how much information English text contains, and why prediction is central to compression.MathematicsAI History
슬라이드1:02EN
3Blue1Brown· 2026년 6월 10일A Robot Walk and the Puzzle of Optimal Binary EncodingHow few bits does it take to send directions that occur with unequal frequencies?MathematicsScience Communication