Application of Monte Carlo Markov chain to determination of hidden Markov model for mobile satellite channels

2005 ◽  
Author(s):  
C. Alasseur ◽  
L. Husson ◽  
F. Perez-Fontan
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Monte Carlo Markov Chain ◽  
Satellite Channels ◽  
Mobile Satellite

scholarly journals The Application of Baum-Welch Algorithm in Multistep Attack

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Yanxue Zhang ◽  
Dongmei Zhao ◽  
Jinxing Liu
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov Models ◽  
Viterbi Algorithm ◽  
Markov Models ◽  
Hidden Markov ◽  
Simulation Experiments ◽  
Forward Algorithm ◽  
Better Than

The biggest difficulty of hidden Markov model applied to multistep attack is the determination of observations. Now the research of the determination of observations is still lacking, and it shows a certain degree of subjectivity. In this regard, we integrate the attack intentions and hidden Markov model (HMM) and support a method to forecasting multistep attack based on hidden Markov model. Firstly, we train the existing hidden Markov model(s) by the Baum-Welch algorithm of HMM. Then we recognize the alert belonging to attack scenarios with the Forward algorithm of HMM. Finally, we forecast the next possible attack sequence with the Viterbi algorithm of HMM. The results of simulation experiments show that the hidden Markov models which have been trained are better than the untrained in recognition and prediction.

scholarly journals Determination of Single Molecule erbB1 Homodimer Lifetimes Using Single Quantum Dot Tracking and a Diffusive Hidden Markov Model

2010 ◽  
Vol 98 (3) ◽  
pp. 498a
Author(s):  
Shalini T. Low-Nam ◽  
Keith A. Lidke ◽  
Patrick J. Cutler ◽  
Rob C. Roovers ◽  
Paul M.P. van Bergen en Henegouwen ◽  
...  
Keyword(s):  
Quantum Dot ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Single Molecule ◽  
Hidden Markov ◽  
Single Quantum ◽  
Single Quantum Dot

Hidden Markov Model with Markovian emission

2020 ◽  
Vol 26 (4) ◽  
pp. 303-313
Author(s):  
Karima Elkimakh ◽  
Abdelaziz Nasroallah
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chains ◽  
Markov Model ◽  
Monte Carlo Methods ◽  
Hidden Markov ◽  
Emission Process ◽  
Numerical Examples ◽  
Proposed Model ◽  
Independent Markov Chain

AbstractIn our paper [A. Nasroallah and K. Elkimakh, HMM with emission process resulting from a special combination of independent Markovian emissions, Monte Carlo Methods Appl. 23 2017, 4, 287–306] we have studied, in a first scenario, the three fundamental hidden Markov problems assuming that, given the hidden process, the observed one selects emissions from a combination of independent Markov chains evolving at the same time. Here, we propose to conduct the same study with a second scenario assuming that given the hidden process, the emission process selects emissions from a combination of independent Markov chain evolving according to their own clock. Three basic numerical examples are studied to show the proper functioning of the iterative algorithm adapted to the proposed model.

Hidden Markov Processes: Basic Properties

2014 ◽  
Author(s):  
M. Vidyasagar
Keyword(s):  
Markov Chain ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Markov Processes ◽  
Viterbi Algorithm ◽  
Hidden Markov ◽  
Basic Properties ◽  
Deterministic Function ◽  
Hidden Markov Processes ◽  
Chain Type

This chapter considers the basic properties of hidden Markov processes (HMPs) or hidden Markov models (HMMs), a special type of stochastic process. It begins with a discussion of three distinct types of HMMs and shows that they are all equivalent from the standpoint of their expressive power or modeling ability: Type 1 hidden Markov model, or a HMM of the deterministic function of a Markov chain type; hidden Markov model of Type 2, or a HMM of the random function of a Markov chain type; and hidden Markov model of Type 3, or a HMM of the joint Markov process type. The chapter also examines various issues related to the computation of likelihoods in a HMM before concluding with an overview of the Viterbi algorithm and the Baum–Welch algorithm.

scholarly journals A Dependent Hidden Markov Model of Credit Quality

2012 ◽  
Vol 2012 ◽  
pp. 1-13 ◽  
Author(s):  
Małgorzata Wiktoria Korolkiewicz
Keyword(s):  
Markov Chain ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Credit Rating ◽  
Hidden Markov ◽  
Credit Ratings ◽  
The State ◽  
Credit Quality ◽  
The Em Algorithm ◽  
Rating Process

We propose a dependent hidden Markov model of credit quality. We suppose that the "true" credit quality is not observed directly but only through noisy observations given by posted credit ratings. The model is formulated in discrete time with a Markov chain observed in martingale noise, where "noise" terms of the state and observation processes are possibly dependent. The model provides estimates for the state of the Markov chain governing the evolution of the credit rating process and the parameters of the model, where the latter are estimated using the EM algorithm. The dependent dynamics allow for the so-called "rating momentum" discussed in the credit literature and also provide a convenient test of independence between the state and observation dynamics.

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