Feynman diagrams without any physics
The article discusses the application of Feynman diagrams in solving problems related to Gaussian random variables. It explains how Isserlis' theorem can be utilized to calculate the expectation values of products of these variables. The author illustrates the process using combinatorial methods and visual representations to simplify complex calculations.
- ▪Feynman diagrams can be applied to elementary probability theory, particularly with Gaussian random variables.
- ▪Isserlis' theorem provides a method for calculating the mean of products of Gaussian variables through perfect matchings.
- ▪The article demonstrates how to visualize and simplify calculations using Feynman diagrams.
Hacker News (Newest) files mainly under programming. We currently carry 5,306 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
inspect →
Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | Github |
| Canonical URL | https://jollywatt.github.io/feynman-diagrams-without-any-physics/ |
| Publication time | Fri, 29 May 2026 08:09:49 +0000 |
| Retrieval time | 2026-05-29T08:19:59.400Z |
| Last seen | 2026-05-29T08:19:59.400Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | hGOHKqzBa8I2 |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
A tale from elementary probability theoryFeynman diagrams are associated with complicated physics, but they can also be found hiding in relatively simple questions about Gaussian random variables.For example, suppose I asked you to calculate the expectation valuewhere each is a (not necessarily independent) normal random variable with zero mean. Say I want the answer in terms of the covariances between the variables.If you are especially visually-minded (and considerably brilliant) you may find yourself eventually drawing the following diagrams……on your way to producing the answer, which is in this case isHere’s some Julia code approximately showing that this formula is correct.julia> using Distributions, Statisticsjulia> Σ = let A = rand(4,4); A'A end # make a symmetric matrix4×4…
Excerpt limited to ~120 words for fair-use compliance. The full article is at Github.
Discussion
0 commentsMore from Github
