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Topic: Hidden Markov model

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Transition matrix

Markov model

Markov chain

Viterbi algorithm

Markov property

Markov process

Examples of Markov chains

Gene regulatory network

Andrei Andreevich Markov

Sequence alignment

Sequence motif

Facial recognition system

Blast

Sequence database

Stochastic matrix

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	Hidden Markov Model
		A Hidden Markov Model or HMM is a type of statistical model in which the system that is being modeled is marked as a Markov process that do not have any known parameters, and the goal or objective is to find out the hidden parameters from the observable parameters.
		In a regular Markov Model, the state is directly visible to the observer and because of this the only parameters used are the state transition probabilities.
		Hidden Markov Models of Bioinformatics by Timo Koski
	www.iscid.org /encyclopedia/Hidden_Markov_Model (227 words)

	Hidden Markov model - Wikipedia, the free encyclopedia
		A hidden Markov model (HMM) is a statistical model where the system being modeled is assumed to be a Markov process with unknown parameters, and the challenge is to determine the hidden parameters from the observable parameters.
		Hidden Markov models are especially known for their application in temporal pattern recognition such as speech, handwriting, gesture recognition and bioinformatics.
		Hidden Markov Models were first described in a series of statistical papers by Leonard E. Baum and other authors in the second half of the 1960s.
	en.wikipedia.org /wiki/Hidden_Markov_model (1153 words)

	HIDDEN MARKOV MODEL:
		In HMMs, a stationary Markov chain is used as this mechanism.
		This self-loop allows HMMs to model the duration of a sound, which is very important because of the high variability in speaking rate for different talkers.
		This type of HMM uses a mixture of Gaussians' to model the relationship between the acoustic frame and the states.
	www.geocities.com /s_madhus/HMM.html (675 words)

	Hidden Markov Model
		A Hidden Markov Model (HMM) is the same as Markov Chain, except that the output symbol as well as the transitions are probabilistic.
		Compared to Markov Chain, the output sequences generated by an HMM are what is known as doubly stochastic: not only is the transitioning from one state to another stochastic (probabilistic), but so is the output symbol generated at each state.
		The Markov nature of the an HMM (namely, that the probability of being in a state is dependent only on the previous state) admits use of the Viterbi algorithm, most likely to have generated the given sequence of symbols, without having to search all possible sequences.
	project.uet.itgo.com /markov_model.htm (1783 words)

	Hidden Markov model
		A hidden Markov model (HMM) is a statistical model where the system being modelled is assumed to be a Markov process with unknown parameters, and the challenge is to determine the hidden parameters of the Markov model based on this assumption.
		The extracted model parameters can then be used to perform further analysis, for example for pattern recognition applications.
		In a regular Markov model, the state is directly visible to the observer, and therefore the state transition probabilities are the only parameters.
	www.ebroadcast.com.au /lookup/encyclopedia/hm/HMM.html (183 words)

	Hidden Markov Models
		Hidden Markov models are used in speech recognition.
		In the hidden case, we use expectation maximization (EM) as described in [Dempster et al., 1977].
		One advantage of this approach is that it extends easily to the case in which the hidden part of the model is factored into some number of state variables.
	www.cs.brown.edu /research/ai/dynamics/tutorial/Documents/HiddenMarkovModels.html (1359 words)

	Hidden Markov Models
		Examples are (hidden) Markov Models of biased coins and dice, formal languages, the weather, etc.; Markov models and Hidden Markov Models (HMM) are used in Bioinformatics to model DNA and protein sequences.
		The issue of the accuracy with which the model's parameters should be stated, and hence the model's complexity, was investigated by Wallace and Boulton (1968, appendix).
		The order of a Markov model of fixed order, is the length of the history or context upon which the probabilities of the possible values of the next state depend.
	www.csse.monash.edu.au /~lloyd/tildeMML/Structured/HMM.html (1021 words)

	Hidden Markov Model
		The HMM for this project was taken from the H2M Matlab Toolbox by Olivier Cappé and modified to suit my needs.
		As this HMM was taken off the shelf I will not delve into the details of explaining the same.
		This was done because in a pragmatic world, the HMM will have to be trained before hand on some data and then used at different noise conditions and various other environment conditions.
	www.cnel.ufl.edu /~kkale/hmm.html (524 words)

	Using Hidden Markov Model in Anomaly Intrusion Detection (Site not responding. Last check: 2007-10-09)
		But for some other processes, the strict assumption of Markov that next state is dependent only upon the current state will not hold, thus we need to find more generally models to deal with these processes while at the same time withhold some good properties of Markov model.
		Hidden Markov Model is a double embedded stochastic process with two hierarchy levels.
		Since our HMM model has modified a lot from original model, the matching criterion is not simply to calculate the probability of the observation sequence by given the model.
	tennis.ecs.umass.edu /~czou/research/HMM/index.htm (4361 words)

	hidden markov model (Site not responding. Last check: 2007-10-09)
		Hidden Markov model (HMM) is distinguished from a general Markov model in that the states in an HMM cannot be observed directly (i.e.
		It is only the outcome, not the state visible to an external observer and therefore states are ``hidden'' to the outside; hence the name Hidden Markov Model.
		Therefore, HMM is used to solve the problem when what we want to predict is not what we observed.
	web.ics.purdue.edu /~jangy/homepg/hmm.html (369 words)

	Appendix 1: The Hidden Markov Model
		A Markov model (MM) models a process that goes through a sequence of discrete states, such as notes in a melody.
		In this model, the probability of transitioning from a given state to another state is assumed to depend only on the current state.
		A model that explicitly maintains a probability distribution over the set of possible observations for each state is called a hidden Markov model (HMM).
	www.dlib.org /dlib/february02/birmingham/birmingham-appendix1.html (1344 words)

	Hidden Markov Model
		In a nutshell, HMM is a probabilistic observation of a Markov chain (MC).
		Model the days by a Markov chain with state space {cold, warm} (Obviously, this is a naive assumption especially at State College).
		The Markov chain for State College's weather condition is as follows: (1) It is of first order and time invariant; (2) If today is cold, the probability it will be cold tomorrow is 0.8, if today is warm, the probability it will be cold tomorrow is 0.5.
	www.stat.psu.edu /~jiali/hmm.html (510 words)

	Hidden Markov Model (HMM) Toolbox for Matlab (Site not responding. Last check: 2007-10-09)
		An HMM is a Markov chain, where each state generates an observation.
		For example, the hidden states may represent words or phonemes, and the observations represent the acoustic signal.
		A tutorial on Hidden Markov Models and selected applications in speech recognition, L.
	www.cs.ubc.ca /~murphyk/Software/HMM/hmm.html (295 words)

	Profile Hidden Markov Model Resources
		Profile HMMs are statistical tools that can model the commonalities of the amino acid sequences for a family of proteins.
		For example, one could train a model on a number of proteins in a family, and then match sequences in a database to that model in order to try to find other family members.
		Hidden Markov Models in Computational Biology: Applications to Protein Modeling Krogh, A., Brown, M., Mian, I.S., Sjolander, K. and Haussler, D. (1994) Journal Mol.
	www.cs.ualberta.ca /~colinc/cmput606 (496 words)

	Hidden Markov models (Site not responding. Last check: 2007-10-09)
		Hidden Markov models (HMMs) are latent variable models based on the Markov chains.
		HMMs are widely used especially in speech recognition and there is an extensive literature on them.
		Before going into details of HMMs, some of the basic properties of Markov chains are first reviewd briefly.
	www.cis.hut.fi /ahonkela/dippa/node32.html (72 words)

	hidden Markov model (Site not responding. Last check: 2007-10-09)
		Usually the states, Q, and outputs, O, are understood, so an HMM is said to be a triple, (A, B, Π).
		Note: Computing a model given sets of sequences of observed outputs is very difficult, since the states are not directly observable and transitions are probabilistic.
		Although the states cannot, by definition, be directly observed, the most likely sequence of sets for a given sequence of observed outputs can be computed in O(nt), where n is the number of states and t is the length of the sequence.
	www.nist.gov /dads/HTML/hiddenMarkovModel.html (284 words)

	Markov Chain and Hidden Markov Model References
		Rabiner, L. R., A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition, Proceedings of the IEEE, vol.
		An exercise of a markov model of the progress of diabetes, with a simulation based on this structure.
		Lecture notes on Markov Chains, some of which are difficult to read.
	www.marypat.org /stuff/random/markov.html (311 words)

	CAVIAR Hidden Semi-Markov Model
		The commonly used algorithms for the HSMM model are O(T^2) meaning that continuous video is computationally infeasible.
		Overall 70% of the situations (individual frames) were correctly classified and 57% of the behaviours (context models) were correctly recognised.
		Overall 74% of the situations (individual frames) were correctly classified and 65% of the behaviours (context models) were correctly recognised.
	homepages.inf.ed.ac.uk /rbf/CAVIAR/hsmm.htm (425 words)

	Drought Frequency Analysis with a Hidden State Markov Model (Site not responding. Last check: 2007-10-09)
		As an alternative to the more conventional ARMA models, we explore the potential of hidden state Markov models for simulating hydrologic droughts.
		In the simplest case, the hidden (unobserved) Markov process has two states, wet and dry, representing the general climate regime prevailing at the time.
		The results of an application of the hidden state Markov model to drought frequency analysis are presented along with a comparison with more conventional models.
	www.pubs.asce.org /WWWdisplay.cgi?0410563 (150 words)

	2.7.5 Hidden Markov Models (Site not responding. Last check: 2007-10-09)
		Note that this is not deterministic, and since the model is hidden, there may in fact be more than one sequence of transitions that resulted in that sequence of symbols.
		Then, using algorithms such as the Baum-Welch algorithm, it is possible to train the HMM by adjusting the weights of the transitions to better model the relationship of the actual training samples.
		Although the HMM technique is different to neural nets, it has many of the same problems, including deciding what the states are.
	www.cse.unsw.edu.au /~waleed/thesis/node39.html (287 words)

	Introduction
		We then look at systems where what we wish to predict is not what we observe - the underlying system is hidden.
		In the above example, the observed sequence would be the seaweed and the hidden system would be the actual weather.
		We then look at some problems that can be solved once the system has been modeled.
	www.comp.leeds.ac.uk /roger/HiddenMarkovModels/html_dev/main.html (325 words)

	4.4.1 Hidden Markov model for parameter estimate
		A hidden Markov model (HMM) with explicit state duration is a doubly stochastic process, whose intensity is controlled by a finite-state discrete-time Markov chain {
		Given an initial set of assumptions for the HMM model parameters (e.g., based on an a posteriori knowledge of the empirical workload), obtain refined estimates of the model parameters by applying the HMM re-estimation algorithm with explicit state duration [
		An often used choice is to assume that these initial values for the model parameters are uniformly distributed.
	www.cs.wm.edu /~riska/PhD-thesis-html/node56.html (465 words)

	Hidden Markov Model Package
		Hidden Markov models are truly an indispensable tool when attempting to recognize temporal or modeling sequences.
		This class implements a hidden Markov model along with it's algorithms including Forward-Backward, Viterbi, K-Means Learner, Baum-Welch, and Kullback-Leibler distance measure.
		We've also created some presentations on hidden Markov models and gesture recognition.
	www.public.asu.edu /~tmcdani/hmm.htm (403 words)

	Phonetic Hidden Markov model speech synthesizer (US5230037)
		The synthesizer is based on the interaction between two different Ergodic Hidden Markov Models: an acoustic model reflecting the constraints on the acoustic arrangement of speech, and a phonetic model interfacing phonemic transcription to the speech features representation.
		A method for generating synthesized speech wherein an acoustic ergodic hidden Markov model (AEHMM) reflecting constraints on the acoustic arrangement of speech is correlated to a phonetic ergodic hidden Markov model (PhEHMM), the method comprising the steps of
		Falaschi, A. et al., "A Hidden Markov Model Approach to Speech Synthesis", Eurospeech Proc.
	www.delphion.com /details?pn=US05230037__ (639 words)

	Myers' Hidden Markov Model software (Site not responding. Last check: 2007-10-09)
		C++ code that implements a basic left-right hidden Markov model and corresponding Baum-Welch (ML) training algorithm.
		It is meant as an example of the HMM algorithms described by L.Rabiner and others.
		The code was built in order to learn how HMM systems work and we are now offering it to the net so that others can learn how to use HMMs for speech recognition.
	www.speech.cs.cmu.edu /comp.speech/Section6/Recognition/myers.hmm.html (148 words)

	SWBIC - Hidden Markov Models (Site not responding. Last check: 2007-10-09)
		Its purpose is to generate hypotheses for structure and function for such hypothetical or putative protein sequences based on fold recognition.
		[Washington University, St. Louis] Pfam is a large collection of multiple sequence alignments and hidden Markov models covering many common protein domains based on the Swissprot 38 and SP-TrEMBL 11 protein sequence databases.
		A library of hidden Markov Models, one per PDB structure and containing approximately 2500 HMMs is on this server.
	www.swbic.org /links/1.11.php (425 words)

	A TUTORIAL ON HIDDEN MARKOV MODELS
		This problem deals with training the HMM such that it encodes the observation sequence in such a way that if a observation sequence having many characteristics similar to the given one be encountered later it should be able to identify it.
		An initial HMM can be constructed in any way but we may use the first five steps of the Segmental K-means Algorithm described above to give us a reasonable initial estimate of the HMM.
		Juang and L. Rabiner, ``The segmental k-means algorithm for estimating the parameters of hidden Markov models,'' IEEE Trans.
	uirvli.ai.uiuc.edu /dugad/hmm_tut.html (3492 words)

	Definition of Hidden Markov Model
		The Hidden Markov Model is a finite set of states, each of which is associated with a (generally multidimensional) probability distribution [].
		In order to define an HMM completely, following elements are needed.
		The number of states of the model, N.
	jedlik.phy.bme.hu /~gerjanos/HMM/node4.html (202 words)

	Automatic Speech Recognition at CSLU
		In standard HMMs, the likelihoods are computed using a Gaussian Mixture Model; in the HMM/ANN framework, these values are computed using an artificial neural network (ANN).
		The Toolkit is written at two levels: the Tcl script level, and the C level.
		However, it is possible that, despite the HMM framework being well-understood, the inaccurate assumptions made when using HMMs for processing speech leads to shortcomings in performance.
	cslu.cse.ogi.edu /asr (1817 words)

	[No title] (Site not responding. Last check: 2007-10-09)
		Hidden Markov Model (HMM) Software: Implementation of Forward-Backward, Viterbi, and Baum-Welch algorithms.
		Postscript slides for tutorial talks that I gave on HMM.
		Here is a system that uses he HMM package for predicting the toplogy of trans membrane helical protiens: [system] [paper]
	www.kanungo.us /software/software.html (212 words)

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