• 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...
    51 KB (6,799 words) - 21:37, 23 September 2024
  • 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...
    5 KB (701 words) - 21:50, 9 January 2024
  • 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...
    10 KB (1,175 words) - 07:55, 18 July 2024
  • The layered hidden Markov model (LHMM) is a statistical model derived from the hidden Markov model (HMM). A layered hidden Markov model (LHMM) consists...
    5 KB (800 words) - 23:17, 7 October 2018
  • Thumbnail for Markov property
    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...
    9 KB (1,211 words) - 07:29, 3 April 2024
  • 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...
    5 KB (567 words) - 00:55, 7 August 2024
  • Thumbnail for Markov chain
    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...
    93 KB (12,558 words) - 10:18, 14 November 2024
  • 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)...
    7 KB (1,025 words) - 16:43, 13 January 2021
  • Thumbnail for Generative pre-trained transformer
    dataset. There were mainly 3 types of early GP. The hidden Markov models learn a generative model of sequences for downstream applications. For example...
    50 KB (4,444 words) - 06:46, 9 November 2024
  • 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)...
    13 KB (1,440 words) - 09:57, 7 November 2024
  • HTK (Hidden Markov Model Toolkit) is a proprietary software toolkit for handling HMMs. It is mainly intended for speech recognition, but has been used...
    1 KB (100 words) - 20:12, 12 October 2024
  • Forward algorithm (category Markov models)
    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...
    15 KB (2,839 words) - 07:32, 10 May 2024
  • Viterbi algorithm (category Markov models)
    done especially in the context of Markov information sources and hidden Markov models (HMM). The algorithm has found universal application in decoding...
    20 KB (2,664 words) - 16:54, 2 November 2024
  • Markov chain, instead of assuming that they are independent identically distributed random variables. The resulting model is termed a hidden Markov model...
    57 KB (7,773 words) - 19:05, 11 November 2024
  • Baum–Welch algorithm (category Markov models)
    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...
    28 KB (3,884 words) - 08:35, 19 October 2024
  • 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...
    122 KB (13,115 words) - 04:44, 11 November 2024
  • 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...
    5 KB (665 words) - 20:52, 12 June 2024
  • Telescoping Markov chain Markov condition Causal Markov condition Markov model Hidden Markov model Hidden semi-Markov model Layered hidden Markov model Hierarchical...
    2 KB (229 words) - 07:10, 17 June 2024
  • Thumbnail for Andrey Markov
    Andrey Markov Chebyshev–Markov–Stieltjes inequalities Gauss–Markov theorem Gauss–Markov process Hidden Markov model Markov blanket Markov chain Markov decision...
    10 KB (1,072 words) - 21:23, 24 October 2024
  • Thumbnail for Map matching
    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...
    8 KB (898 words) - 05:27, 17 June 2024
  • 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...
    2 KB (315 words) - 18:10, 13 January 2021
  • Thumbnail for Expectation–maximization algorithm
    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...
    51 KB (7,595 words) - 14:31, 6 November 2024
  • 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...
    70 KB (8,082 words) - 06:22, 9 October 2024
  • Part-of-speech tagging (category Markov models)
    algorithm (also known as the forward-backward algorithm). Hidden Markov model and visible Markov model taggers can both be implemented using the Viterbi algorithm...
    16 KB (2,266 words) - 02:30, 11 May 2024
  • fragment Heterogeneous memory management, in the Linux kernel Hidden Markov model, a statistical model Central Mashan Miao language (ISO 639-3 code), spoken in...
    1 KB (162 words) - 20:43, 8 November 2024
  • 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...
    7 KB (1,162 words) - 17:14, 30 October 2024
  • Entropy rate (category Markov models)
    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...
    5 KB (784 words) - 18:08, 6 November 2024
  • types of mixture model) Hidden Markov model Probabilistic context-free grammar Bayesian network (e.g. Naive bayes, Autoregressive model) Averaged one-dependence...
    19 KB (2,434 words) - 03:43, 14 August 2024
  • Thumbnail for Bivalent (genetics)
    differentiating cells, we build a pseudo time approximation and use a hidden Markov model to infer gene activity switching pseudo times, which we use to infer...
    10 KB (1,329 words) - 16:22, 22 September 2024
  • statistics, econometrics, and signal processing, an autoregressive (AR) model is a representation of a type of random process; as such, it can be used...
    34 KB (5,421 words) - 22:47, 5 November 2024