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LATENT REFERENCES / TAG2

Markov Chain

Archive curator and editor: Keigo Yoshida

This reference note belongs to Tag2 in Latent References, an archive curated by Keigo Yoshida. Its archive region is Analysis. The note preserves its source text and links so that readers can trace the material behind the 3D map.

Collection
Tag2
Archive region
Analysis

Archived reference note

English translation of the archived note. JP shows the original text. Source links and literal code are retained; the translation does not update or independently verify the source claims.

A Markov chain (Japanese reading: Marukofu rensa; English: Markov chain) is a Markov process, a type of stochastic process, whose possible states are discrete (finite or countable), i.e., a discrete-state Markov process. In particular, it often refers to one with discrete time (times represented by subscripts). In a Markov chain, future behavior is determined only by the present value and is independent of past behavior (the Markov property). Regarding changes of state (transitions) occurring at each time, it is a sequence whose transition probabilities depend only on the current state, not on past states. An especially important stochastic process, it is applied in various fields. https://scrapbox.io/files/648b41a2d9078e001bc7f9d3.png

Source updated 2023-06-15 · Snapshot 2026-10-08

Source links and calculated neighbors

Cosine values measure shared lexical features, not truth, agreement or identical meaning. Original reference links are labeled separately.

  • Markov ChainComputed lexical cosine similarity 0.652 · shared title, text, tags and references
  • Markov Decision ProcessComputed lexical cosine similarity 0.204 · shared title, text, tags and references
  • Hidden Markov ModelComputed lexical cosine similarity 0.187 · shared title, text, tags and references
  • Tarkovsky: MirrorComputed lexical cosine similarity 0.148 · shared title, text, tags and references