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...

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)...

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

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...

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

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...

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

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

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

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...

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

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...

dataset. There were mainly 3 types of early GP. The hidden Markov models learn a generative model of sequences for downstream applications. For example, in...

A number of different Markov models of DNA sequence evolution have been proposed. These substitution models differ in terms of the parameters used to...

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...

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

style, but could be learned using machine learning methods such as Markov models. Researchers have generated music using a myriad of different optimization...

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

statistics, econometrics, and signal processing, an autoregressive (AR) model is a representation of a type of random process; as such, it can be used...

deterioration modeling. Recently, more complex methods based on simulation, Markov models and machine learning models have been introduced. A well-known model to...

matched pair of indices, between their values.[citation needed] A hidden Markov model can be represented as the simplest dynamic Bayesian network. The goal...

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...

events. This is done especially in the context of Markov information sources and hidden Markov models (HMM). The algorithm has found universal application...

types of mixture model) Hidden Markov model Probabilistic context-free grammar Bayesian network (e.g. Naive bayes, Autoregressive model) Averaged one-dependence...

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 from...

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...

Heterogeneous memory management, in the Linux kernel Hidden Markov model, a statistical model Central Mashan Miao language (ISO 639-3 code), spoken in China...

In mathematics, the quantum Markov chain is a reformulation of the ideas of a classical Markov chain, replacing the classical definitions of probability...