Ranté Markov: Béda antarrépisi

Konten dihapus Konten ditambahkan
Ilhambot (obrolan | kontribusi)
m Ngarapihkeun éjahan, replaced: oge → ogé (2), nyaeta → nyaéta (4), rea → réa, ngarupakeun → mangrupa, ea → éa (7), eo → éo, Didinya → di dinya
Ilhambot (obrolan | kontribusi)
m Ngarapihkeun éjahan, replaced: jentre → jéntré (2), model → modél (3)
Baris ka-22:
nu disebut prosés ''transition probability''.
Kadangkala disebut ogé "one-step" transition probability.
''Transition probability'' dua tahap, tilu tahap atawa leuwih dijentrekeundijéntrékeun tina ''transition probability'' satahap sarta sifat Markov:
 
: <math> P(X_{n+2}|X_n) = \int P(X_{n+2},X_{n+1}|X_n)\,dX_{n+1}
Baris ka-35:
[[Marginal distribution]] ''P''(''X''<sub>''n''</sub>) nyaéta distribusi nu ditangtukeun dina waktu ''n''.
Distiribusi awal nyaéta ''P''(''X''<sub>0</sub>).
Evolusi proses ngaliwatan sakali tahap waktu nu dijentrekeundijéntrékeun ku
 
: <math> P(X_{n+1}) = \int P(X_{n+1}|X_n)\,P(X_n)\,dX_n </math>
Baris ka-113:
== Scientific applications ==
 
Markov chains are used to modelmodél various processes in [[queueing theory]] and [[statistics]], and can also be used as a signal modelmodél in [[entropy coding]] techniques such as [[arithmetic coding]]. Markov chains also have many biological applications, particularly [[population process]]es, which are useful in modellingmodélling processes that are (at léast) analogous to biological populations. Markov chains have been used in [[bioinformatics]] as well. An example is the [[genemark algorithm]] for coding region/gene prediction.
 
Markov processes can also be used to generate superficially "real-looking" text given a sample document: they are used in various pieces of recréational "parody generator" software (see [[Jeff Harrison]]).