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...

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...

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...

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...

A hidden 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...

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

been modeled using Markov chains, also including modeling the two states of clear and cloudiness as a two-state Markov chain. Hidden Markov models have...

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)...

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

HTK (Hidden Markov Model Toolkit) is a proprietary software toolkit for handling HMMs. It is mainly intended for speech recognition, but has been used...

bottom-up, and top-down methods. Probabilistic methods based on hidden Markov models have also proved useful in solving this problem. It is often the...

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...

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

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...

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

requires substantial processing time. Map matching is described as a hidden Markov model where emission probability is a confidence of a point to belong a...

The forward algorithm, in the context of a hidden Markov model (HMM), is used to calculate a 'belief state': the probability of a state at a certain time...

unculturable bacteria) based on a model of already labeled data. Hidden Markov models (HMMs) are a class of statistical models for sequential data (often related...

needed] A hidden Markov model can be represented as the simplest dynamic Bayesian network. The goal of the algorithm is to estimate a hidden variable x(t)...

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

statistics, a hidden Markov random field is a generalization of a hidden Markov model. Instead of having an underlying Markov chain, hidden Markov random fields...

appropriate α. The α-EM algorithm leads to a faster version of the Hidden Markov model estimation algorithm α-HMM. EM is a partially non-Bayesian, maximum...

are two primary classes of Markov models, visible Markov models and hidden Markov models, which differ in whether the Markov chain generating token sequences...

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

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

manifestations of a hidden Markov model (HMM), which means the true state x {\displaystyle x} is assumed to be an unobserved Markov process. The following...

algorithm (also known as the forward-backward algorithm). Hidden Markov model and visible Markov model taggers can both be implemented using the Viterbi algorithm...

rate of hidden Markov models (HMM) has no known closed-form solution. However, it has known upper and lower bounds. Let the underlying Markov chain X...

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

context-dependent Hidden Markov model (HMM). Major search methods are fully incorporated. It is also modularized carefully to be independent from model structures...