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Topic: Markov model

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Markov chain

Markov property

Hidden Markov model

Examples of Markov chains

Andrei Andreevich Markov

Brownian motion

Natural language processing

Thirtieth Street Station

Turkish music (style)

30th Street Rail Station

Crucifixion

30th Street Station

	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.
		In a regular Markov model, the state is directly visible to the observer, and therefore the state transition probabilities are the only parameters.
		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 (1124 words)

	Markov model (Site not responding. Last check: 2007-11-01)
		Markov chains are related to Brownian motion and the ergodic hypothesis, two topics in physics which were important in the early years of the twentieth century, but Markov appears to have pursued this out of amathematical motivation, namely the extension of the law oflarge numbers to dependent events.
		Markov chains are used to model various processes in queueingtheory and statistics, and can also be used as a signal model in entropy coding techniques such as arithmetic coding.
		Markov chains also have many biological applications, particularly population processes, which are useful in modelling processes that are(at least) analogous to biological populations.
	www.therfcc.org /markov-model-203197.html (910 words)

	Hidden Markov Models (Site not responding. Last check: 2007-11-01)
		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)

	Floor Management Netwerk - Tools - Markov Model - Analizing complex systems
		Markov models are some of the most powerful tools available to engineers and scientists for analyzing complex systems.
		Markov models are applicable to systems with common cause failures, such as an electrical lightning storm shock to a computer system.
		Markov models can handle degradation, as may be the case with a mechanical system.
	www.floor.nl /management/markov.html (445 words)

	The hidden Markov model (Site not responding. Last check: 2007-11-01)
		A state shown as a rectangular box is a match state that models the distribution of letters in the corresponding column of an alignment.
		A state shown by a diamond-shaped box models insertions of random letters between two alignment positions, and a state shown by a circle models a deletion, corresponding to a gap in an alignment.
		Alignment of a sequence to a model means that each letter in the sequence is associated with a match or insert state in the model.
	www.cse.ucsc.edu /research/compbio/html_format_papers/hughkrogh96/node4.html (707 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 (Site not responding. Last check: 2007-11-01)
		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, fromthe observable parameters, based on this assumption.
		This analogy is particularly strong when modelling parts of speech andsentences, and other entities which have a strongly defined semantic meaning independent of the myriad of possiblerepresentations in the observable sequence.
		In a regular Markov model, the state is directly visible to the observer, and therefore the state transition probabilities arethe only parameters.
	www.therfcc.org /hidden-markov-model-79185.html (349 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)

	Markov, Markov Model & Markov Analysis Software - Relex Software
		Markov models are a flexible system modeling tool that enables an analyst to consider scenarios that are not traditionally supported by other classes of analyses.
		Markov models are used to consider systems where future performance is determined solely by its current state.
		Markov models generally are displayed in a simple graphic form, but require extensive mathematics and time to complete even simple models.
	www.relex.com /resources/markov.asp (188 words)

	Markov Model Exercise (Site not responding. Last check: 2007-11-01)
		To build a markov model, we must specify a set of states, probabilities that a patient will move from one state to another, and the utilities of living in each state.
		This is obviously a simplification, and it's possible to build Markov models that allow (for example) a chance of death that increases over time (background mortality).
		Imagine that the utility for living with controlled diabetes is 0.95, the utility for ESRD is 0.30, and the utility of death is, by definition, 0.
	www.pennmush.org /~alansz/courses/mdm-block/markov.html (552 words)

	Markov Analysis Software, Markov Process & Model - Relex Software
		Markov analysis techniques can be applied to a wide variety of engineering applications where system states need to be taken into account.
		Markov analyses study dependent random events—events whose likelihood depends on what happened last, or where the sequence of occurrence must be considered for analysis.
		Markov modeling enables you to account for combinations of system complexities such as standby failures, non-standard common cause failures, induced failure and shared load systems, imperfect fault coverage and switch-over mechanisms, repair priorities, limited repair resources, and failure sequence dependent consequences.
	www.relex.com /products/markov.asp (691 words)

	Asymptotic Rate of an Open Loop Markov Model
		However, for more complicated models the open-loop hazard rate for the nth state is a function of time, so in general it isn’t clear, a priori, how to infer the steady-state closed-loop hazard rate (for the nth state) from the open-loop behavior.
		Open Loop and Closed Loop Markov Models for a derivation of this result.) If, on the other hand, we decide to treat this as an open loop model, allowing all probability to eventually accumulate in State 2, what is the asymptotic hazard rate for State 2? Taking P
		In general, for any Markov model, the asymptotic open loop rate is always equal to the negative of the dominant eigenvalue, where "dominant" means the smallest absolute value.
	www.mathpages.com /home/kmath559/kmath559.htm (1162 words)

	generation5 - An Introduction to Markov Models
		Markov models are excellent ways of abstracting simple concepts into a relatively easily computable form.
		Markov models are used in everything from data compression to sound recognition.
		When the computer checks it's Markov Model is would find that the first phrases is (unfortunately) much more likely to appear than the second, so it uses as a base for the rest of the sentence.
	www.generation5.org /content/2001/markov.asp (736 words)

	Hidden markov model - Wikipedia, the free encyclopedia (Site not responding. Last check: 2007-11-01)
		Start the Hidden markov model article or add a request for it.
		Look for "Hidden markov model" in the Wikimedia Commons, our repository for free images, music, sound, and video.
		Promotional articles about yourself, your friends, your company or products; or articles written as part of a marketing or promotional campaign, may be deleted in accordance with our deletion policies.
	www.sciencedaily.com /encyclopedia/hidden_markov_model (188 words)

	Markov Model of Natural Language
		Markov chains are now widely used in speech recognition, handwriting recognition, information retrieval, data compression, and spam filtering.
		We can fit a Markov model of order 0 to a specific piece of text by counting the number of occurrences of each letter in that text, and using these counts as probabilities.
		For the purpose of this assignment, a Markov chain is comprised of a set of states, one distinguished state called the start state, and a set of transitions from one state to another.
	www.cs.princeton.edu /courses/archive/fall04/cos126/assignments/markov.html (2268 words)

	BioMed Central \| Abstract \| Generalizations of Markov model to characterize biological sequences
		Using di- and tri-nucleotide as the model unit significantly improved the sequence classification accuracy relative to the standard single nucleotide model.
		Markov modeling is an important component of many sequence analysis tools.
		The proposed generalizations of the Markov model are likely to improve the overall accuracy of sequence analysis tools.
	www.biomedcentral.com /1471-2105/6/219/abstract (287 words)

	Hidden Markov Models
		A discrete-time, discrete-space dynamical system governed by a Markov chain emits a sequence of observable outputs: one output (observation) for each state in a trajectory of such states.
		Hidden Markov models are used in speech recognition.
		To estimate the lambda parameters for this Markov chain it is enough just to calculate the appropriate frequencies from the observed sequence of outputs.
	www.cs.brown.edu /research/ai/dynamics/tutorial/Documents/HiddenMarkovModels.html (1359 words)

	Markov model (Site not responding. Last check: 2007-11-01)
		The beautiful model had her face on the cover of almost every fashion magazine imaginable.
		The sculptor modeled the clay into the form of a dolphin.
		If you are just starting to explore a career in modeling then the first thing you should do is to create a photography portfolio.
	www.serebella.com /encyclopedia/article-Markov_model.html (501 words)

	Markov Model
		In the standard binary logistic-normal model, we have
		A Markovian structure may be built into the model by generalising to
		Then the contribution to the likelihood by the ith case and tth event is given by
	www.cas.lancs.ac.uk /software/sabre3.1/sabre/node8.html (73 words)

	Using Hidden Markov Model in Anomaly Intrusion Detection (Site not responding. Last check: 2007-11-01)
		The Hidden Markov Model is a finite set of states, each of which is associated with a (generally multidimensional) probability distribution.
		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.
		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)

	Forecast of Tropical Pacific SST Using a Markov Model
		Forecasts of the tropical Pacific SST anomaly are presented here using a linear statistical model (Markov model).
		The Markov model is constructed in a reduced multivariate EOF space of observed sea surface temperature (SST), surface wind stress and sea level analysis (Xue et al.
		The Markov model is built with three multivariate EOFs in which the anomalous fields of SST, wind stress and sea level are equally weighted.
	grads.iges.org /ellfb/Mar06/xue/xue.htm (541 words)

	Markov Baseball Models Theory
		A Markov chain is a mathematical model that can be thought of a being in exactly one of a number of states at any time.
		The key output of the Markov chain baseball model is the computation of the expected runs in the remainder of the inning after any runners and outs state.
		Ignoring errors on foul pops, which are not relevant to Markov analysis, the only situations that can't be reached by a non-batter play are those with the bases full (#22-24) and the third out, three runs scored (#28).
	www.pankin.com /markov/theory.htm (2854 words)

	Corpora List Apr 1995 to Jun 1995: Re: What makes a Markov model hidden?
		the advantage of using a visible markov model with tagged data is that
		Model [1], how can those taggers be regarded as markov model
		markov model for tagging and training was based on hand tagged data.
	torvald.aksis.uib.no /corpora/1995-2/0214.html (852 words)

	MARCA_Models.
		Secondly, the models may prove to be useful to those engaged in system modelling, for users can modify the model parameters to obtain others that more closely correspond to their own requirements.
		The specific model chosen is a biological model: the general epidemic model of Ridler-Rowe.
		The files that are needed for a specific model are tar'd and gzip'd into a file named after the example e.g., ncd.tar.gz, mutex.tar.gz, etc. When gunzip'd and untar'd each produces a directory that bears the model's name.
	www.csc.ncsu.edu /faculty/stewart/MARCA_Models/MARCA_Models.html (602 words)

	Bioline International Official Site (site up-dated regularly)
		The study of nucleotide substitution is very important both to our understanding of gene evolution and to reliable estimation of phylogenetic relationships.
		In this paper nucleotide substitution is assumed to be random and the Markov model is applied to the study of the evolution of genes.
		One of the most important conclusions from this work is that gene sequence evolution conforms to the Markov process.
	www.bioline.org.br /abstract?id=ts01086 (136 words)

	Markov model for evolution in DNA and proteins. (Site not responding. Last check: 2007-11-01)
		Markov model for evolution in DNA and proteins.
		Evolution at each position is governed by a homogeneous Markov process (usually modeled as discrete).
		The Markov process is the same for all positions.
	www.bioinfo.rpi.edu /~zukerm/Bio-5495/phylo-html/node4.html (172 words)

	Definition of Hidden Markov model
		The preceding diagram emphasizes the state transitions of a hidden Markov model.
		It is also useful to explicitly represent the evolution of the model over time, with the states at different times t
		A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition.
	www.wordiq.com /definition/Hidden_Markov_model (795 words)

	hidden Markov model (Site not responding. Last check: 2007-11-01)
		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.
		Named after Andrei Andreyevich Markov (1856 - 1922), who studied poetry and other texts as stochastic sequences of characters.
	www.nist.gov /dads/HTML/hiddenMarkovModel.html (284 words)

	Hidden Markov Model (HMM) Toolbox for Matlab (Site not responding. Last check: 2007-11-01)
		An HMM is a Markov chain, where each state generates an observation.
		A tutorial on Hidden Markov Models and selected applications in speech recognition, L.
		Factorial Hidden Markov Models, Z. Ghahramani and M. Jordan, Machine Learning 29:245--273, 1997.
	www.cs.ubc.ca /~murphyk/Software/HMM/hmm.html (285 words)

	Profile Hidden Markov Model Resources
		Once a model is trained on a number of amino acid sequences from a given family or group, it is most commonly used for three purposes:
		The model can be used to score how well a new protein sequence fits the family motif.
		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.
	www.cs.ualberta.ca /~colinc/cmput606 (496 words)

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