Что (кто) такое Markov chain - определение

STOCHASTIC MODEL DESCRIBING A SEQUENCE OF POSSIBLE EVENTS IN WHICH THE PROBABILITY OF EACH EVENT DEPENDS ONLY ON THE STATE ATTAINED IN THE PREVIOUS EVENT

Markov process; Markov sequence; Markov chains; Markov analysis; Markovian process; Markovian property; Markov predictor; Markoff chain; Markov Chain; Markoff Chain; Transition probabilities; Absorbing state; Markov Chaining; Equilibrium distribution; Markov-Chain; Markhow chain; Irreducible Markov chain; Transition probability; Markov Chains; Homogeneous Markov chain; Markov Processes; Markov Sequences; Markov Process; Markovian chain; Embedded Markov chain; Positive recurrent; Transition density; Transitional probability; Markov text generators; Markov text; Applications of Markov chains

Markov chain

<probability> (Named after Andrei Markov) A model of sequences of events where the probability of an event occurring depends upon the fact that a preceding event occurred. A Markov process is governed by a Markov chain. In simulation, the principle of the Markov chain is applied to the selection of samples from a probability density function to be applied to the model. Simscript II.5 uses this approach for some modelling functions. [Better explanation?] (1995-02-23)

Markov process

<probability, simulation> A process in which the sequence of events can be described by a Markov chain. (1995-02-23)

Markov chain Monte Carlo

CLASS OF ALGORITHMS

Random walk Monte Carlo; Markov Chain Monte Carlo; Markov chain monte carlo; Monte Carlo markov chain; Markov clustering; MCMC methods; MCMC method; Markov Chain Monte Carlo Simulations; Markov chain Monte Carlo method; Markov chain Monte Carlo methods

In statistics, Markov chain Monte Carlo (MCMC) methods comprise a class of algorithms for sampling from a probability distribution. By constructing a Markov chain that has the desired distribution as its equilibrium distribution, one can obtain a sample of the desired distribution by recording states from the chain.

https://en.wikipedia.org/wiki/Markov_chain_Monte_Carlo

Википедия

Markov chain

A Markov chain or Markov process is a stochastic model describing a sequence of possible events in which the probability of each event depends only on the state attained in the previous event. Informally, this may be thought of as, "What happens next depends only on the state of affairs now." A countably infinite sequence, in which the chain moves state at discrete time steps, gives a discrete-time Markov chain (DTMC). A continuous-time process is called a continuous-time Markov chain (CTMC). It is named after the Russian mathematician Andrey Markov.

Markov chains have many applications as statistical models of real-world processes, such as studying cruise control systems in motor vehicles, queues or lines of customers arriving at an airport, currency exchange rates and animal population dynamics.

Markov processes are the basis for general stochastic simulation methods known as Markov chain Monte Carlo, which are used for simulating sampling from complex probability distributions, and have found application in Bayesian statistics, thermodynamics, statistical mechanics, physics, chemistry, economics, finance, signal processing, information theory and speech processing.

The adjectives Markovian and Markov are used to describe something that is related to a Markov process.

https://en.wikipedia.org/wiki/Markov chain

Примеры произношения для Markov chain

1. like Markov chain Monte Carlo, and large data, and whatnot,

The Master Algorithm _ Pedro Domingos _ Talks at Google

2. And they wrote their watershed synthesis, now called MCMC for Markov chain-Monte Carlo,

The Theory That Would Not Die _ Sharon Bertsch McGrayne _ Talks at Google