In probability theory, a Markov model is a stochastic model used to model pseudo-randomly changing systems. It is assumed that future states depend only...

A hidden Markov model (HMM) is a Markov model in which the observations are dependent on a latent (or hidden) Markov process (referred to as X {\displaystyle...

honor of the Russian mathematician Andrey Markov. Markov chains have many applications as statistical models of real-world processes. They provide the...

In statistics, the Gauss–Markov theorem (or simply Gauss theorem for some authors) states that the ordinary least squares (OLS) estimator has the lowest...

In statistics, Markov chain Monte Carlo (MCMC) is a class of algorithms used to draw samples from a probability distribution. Given a probability distribution...

maximum-entropy Markov model (MEMM), or conditional Markov model (CMM), is a graphical model for sequence labeling that combines features of hidden Markov models (HMMs)...

The term Markov assumption is used to describe a model where the Markov property is assumed to hold, such as a hidden Markov model. A Markov random field...

The layered hidden Markov model (LHMM) is a statistical model derived from the hidden Markov model (HMM). A layered hidden Markov model consists of N levels...

Markov decision process (MDP), also called a stochastic dynamic program or stochastic control problem, is a model for sequential decision making when...

The hierarchical hidden Markov model (HHMM) is a statistical model derived from the hidden Markov model (HMM). In an HHMM, each state is considered to...

Telescoping Markov chain Markov condition Causal Markov condition Markov model Hidden Markov model Hidden semi-Markov model Layered hidden Markov model Hierarchical...

Andrey Markov Chebyshev–Markov–Stieltjes inequalities Gauss–Markov theorem Gauss–Markov process Hidden Markov model Markov blanket Markov chain Markov decision...

In the mathematical theory of probability, an absorbing Markov chain is a Markov chain in which every state can reach an absorbing state. An absorbing...

semi-Markov model (HSMM) is a statistical model with the same structure as a hidden Markov model except that the unobservable process is semi-Markov rather...

and probability, a Markov random field (MRF), Markov network or undirected graphical model is a set of random variables having a Markov property described...

A language model is a model of the human brain's ability to produce natural language. Language models are useful for a variety of tasks, including speech...

graphical model is known as a directed graphical model, Bayesian network, or belief network. Classic machine learning models like hidden Markov models, neural...

contains examples of Markov chains and Markov processes in action. All examples are in the countable state space. For an overview of Markov chains in general...

Markov renewal processes are a class of random processes in probability and statistics that generalize the class of Markov jump processes. Other classes...

Markov chain, instead of assuming that they are independent identically distributed random variables. The resulting model is termed a hidden Markov model...

stochastic matrix is a square matrix used to describe the transitions of a Markov chain. Each of its entries is a nonnegative real number representing a probability...

expectation–maximization algorithm used to find the unknown parameters of a hidden Markov model (HMM). It makes use of the forward-backward algorithm to compute the...

Reddy's students James Baker and Janet M. Baker began using the hidden Markov model (HMM) for speech recognition. James Baker had learned about HMMs during...

In bioinformatics, GLIMMER (Gene Locator and Interpolated Markov ModelER) is used to find genes in prokaryotic DNA. "It is effective at finding genes in...

theory, a Markov reward model or Markov reward process is a stochastic process which extends either a Markov chain or continuous-time Markov chain by adding...

In the mathematical theory of random processes, the Markov chain central limit theorem has a conclusion somewhat similar in form to that of the classic...

diffusion model can be sampled in many ways, with different efficiency and quality. There are various equivalent formalisms, including Markov chains, denoising...

system. This concept is central in probabilistic graphical models and feature selection. If a Markov blanket is minimal—meaning that no variable in it can...

age-dependent point process model and the two-state Markov Model. Berry and Meister studied neuronal refractoriness using a stochastic model that predicts spikes...

often called the Viterbi path. It is most commonly used with hidden Markov models (HMMs). For example, if a doctor observes a patient's symptoms over...