Markov processes
E48274
Markov processes are stochastic processes in which the future evolution depends only on the present state and not on the past history.
All labels observed (11)
| Label | Occurrences |
|---|---|
| Markov processes canonical | 10 |
| Markov chains | 7 |
| Markov property | 7 |
| Markov chain | 5 |
| Markov chain theory | 2 |
| DTMC | 1 |
| Gaussian Markov process | 1 |
| Markov decision processes | 1 |
| Markov jump process | 1 |
| Markov process | 1 |
| Markov process theory | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T378952 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Markov processes Context triple: [Fokker–Planck equation, models, Markov processes]
-
A.
Markov chain Monte Carlo
Markov chain Monte Carlo is a class of algorithms that uses Markov chains to generate samples from complex probability distributions, widely used in Bayesian inference, statistical physics, and machine learning.
-
B.
Brownian motion
Brownian motion is the random, jittery movement of microscopic particles suspended in a fluid, whose explanation provided key evidence for the existence of atoms and the molecular nature of matter.
-
C.
Feynman–Kac formula
The Feynman–Kac formula is a fundamental result connecting solutions of certain partial differential equations with expectations over stochastic processes, forming a bridge between quantum mechanics, probability theory, and mathematical finance.
-
D.
Fokker–Planck equation
The Fokker–Planck equation is a partial differential equation that describes the time evolution of the probability density function of a stochastic (random) process, such as Brownian motion.
-
E.
Itô calculus
Itô calculus is a branch of stochastic analysis that extends classical calculus to functions of stochastic processes, particularly Brownian motion, enabling rigorous treatment of stochastic differential equations.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Markov processes Target entity description: Markov processes are stochastic processes in which the future evolution depends only on the present state and not on the past history.
-
A.
Markov chain Monte Carlo
Markov chain Monte Carlo is a class of algorithms that uses Markov chains to generate samples from complex probability distributions, widely used in Bayesian inference, statistical physics, and machine learning.
-
B.
Brownian motion
Brownian motion is the random, jittery movement of microscopic particles suspended in a fluid, whose explanation provided key evidence for the existence of atoms and the molecular nature of matter.
-
C.
Feynman–Kac formula
The Feynman–Kac formula is a fundamental result connecting solutions of certain partial differential equations with expectations over stochastic processes, forming a bridge between quantum mechanics, probability theory, and mathematical finance.
-
D.
Fokker–Planck equation
The Fokker–Planck equation is a partial differential equation that describes the time evolution of the probability density function of a stochastic (random) process, such as Brownian motion.
-
E.
Itô calculus
Itô calculus is a branch of stochastic analysis that extends classical calculus to functions of stochastic processes, particularly Brownian motion, enabling rigorous treatment of stochastic differential equations.
- F. None of above. chosen
Statements (50)
| Predicate | Object |
|---|---|
| instanceOf |
mathematical concept
ⓘ
stochastic process ⓘ |
| canBe |
time-homogeneous
ⓘ
time-inhomogeneous ⓘ |
| canHave |
continuous state space
ⓘ
countable state space ⓘ discrete state space ⓘ finite state space ⓘ uncountable state space ⓘ |
| characterizedBy |
initial distribution
ⓘ
state space ⓘ transition kernel ⓘ transition probabilities ⓘ |
| contrastsWith | non-Markovian process ⓘ |
| dependsOn | current state only ⓘ |
| doesNotDependOn | past history given present state ⓘ |
| field |
probability theory
ⓘ
stochastic processes ⓘ |
| formalizedAs | family of random variables indexed by time ⓘ |
| generalizationOf |
Markov chain
ⓘ
linked to:
Markov processes
|
| hasApplication |
modeling diffusion of particles
ⓘ
modeling population dynamics ⓘ modeling queues ⓘ modeling random walks ⓘ modeling stock prices ⓘ |
| hasProperty |
Markov property
ⓘ
linked to:
Markov processes
Markov semigroup (in time-homogeneous case) ⓘ memoryless ⓘ |
| hasSubtype |
Markov chain
ⓘ
Markov decision process ⓘ Markov jump process ⓘ
linked to:
Markov processes
birth–death process ⓘ continuous-time Markov process ⓘ diffusion process ⓘ discrete-time Markov process ⓘ hidden Markov model ⓘ |
| namedAfter | Andrey Markov ⓘ |
| relatedTo |
Chapman–Kolmogorov equation
ⓘ
Kolmogorov backward equation ⓘ Kolmogorov forward equation ⓘ
linked to:
Fokker–Planck equation
|
| timeIndex |
continuous time
ⓘ
discrete time ⓘ |
| usedIn |
biology
ⓘ
control theory ⓘ finance ⓘ information theory ⓘ queueing theory ⓘ reinforcement learning ⓘ signal processing ⓘ statistical physics ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: Markov processes Description of subject: Markov processes are stochastic processes in which the future evolution depends only on the present state and not on the past history.
Referenced by (37)
Full triples — surface form annotated when it differs from this entity's canonical label.
linked to: Markov processes
linked to: Markov processes
subject linked to:
Markov processes
linked to: Markov processes
subject linked to:
Markov processes
linked to: Markov processes
subject linked to:
Markov processes
linked to: Markov processes
linked to: Markov processes
linked to: Markov processes
subject linked to:
PageRank algorithm
linked to: Markov processes
linked to: Markov processes
linked to: Markov processes
subject linked to:
Probability Theory
linked to: Markov processes
subject linked to:
Probability Theory
linked to: Markov processes
linked to: Markov processes
linked to: Markov processes
linked to: Markov processes
linked to: Markov processes
linked to: Markov processes
linked to: Markov processes
subject linked to:
Markov random fields
linked to: Markov processes
linked to: Markov processes
linked to: Markov processes
linked to: Markov processes
linked to: Markov processes
linked to: Markov processes
linked to: Markov processes
linked to: Markov processes
subject linked to:
Sheldon M. Ross (born Sheldon M. Frisch)
linked to: Markov processes