Markov chain-based methods
Subgradient-Based Markov Chain Monte Carlo Particle Methods for Discrete-Time Nonlinear Filtering
... Each pair {x (i) 0:k , w k (i) } characterises the belief that the system is in state x (i) 0:k . An estimate of the variable of interest is obtained by the weighted sum of particles. Two major stages can be ...
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Information geometric Markov chain Monte Carlo methods using diffusions
... Konukoglu et al. [56] designed a method for personalising a generic model for a physiological process to a specific patient, using clinical data. The personalisation took the form of patient-specific parameter inference. ...
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Estimation of Population Parameters and Recombination Rates From Single Nucleotide Polymorphisms
... Bayesian methods for analyzing single nucleotide polymorphisms (SNPs) are ...population based on 37 SNP ...a Markov chain Monte Carlo (MCMC) method for analyzing linked SNPs is ...Both ...
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On solving integral equations using Markov chain Monte Carlo methods
... dimension. Based on this representation, it is possible to use trans-dimensional Markov Chain Monte Carlo (MCMC) methods such as Reversible Jump MCMC to approximate the solution ...(SIS) ...
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A control chart using copula based Markov chain models
... copula-based Markov chain to perform statistical process control for correlated ...three methods to obtain the estimates of upper control limit (UCL) and lower control limit (LCL) for the ...
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A New Model to Speculate CLV Based on Markov Chain Model
... mining methods to understand the effective parameters that shape customer ...2, Markov chain model can be a good choice. In Markov chain model, states must be defined in a form that ...
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Markov Chain methods for the Bipartite Boolean Quadratic Programming Problem
... matrices), which would compete with the state-of-the-art meth- ods on the benchmark instances (which have sizes 200 × 10 0 0 to 50 0 0 × 50 0 0) and with running times in the order of several seconds to several minutes. ...
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Exploring the Impact of Work Life Balance on the Employee and Organisational Growth
... simulation methods based on Markov chain Monte Carlo (MCMC) make possible the routine Bayesian analysis of Two Phase Linear Regression ...
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An Experiment of Markov Chain and N Gram Methods for Reconstructing a Music
... Abstract: Music is an art that relies on intuition and creativity to make music. Making a musical composition is an ordinary work done by a musician. Now a days, an algorithm can make a composition of music based ...
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A non iterative (trivial) method for posterior inference in stochastic volatility models
... use Markov Chain Monte Carlo (MCMC) methods which can be difficult to converge due to inherent ...are based on Gibbs sampling using θ|h, Y which is trivial and h t |h t−1 , h t+1 , θ, Y ...
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On Markov chain Monte Carlo methods for tall data
... whole dataset. Frequentist or variational Bayes approaches are thus usually preferred to a fully Bayesian analysis in the tall data context on computational grounds. However, they might be difficult to put in practice or ...
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Stability of sequential Markov Chain Monte Carlo methods
... time-homogeneous Markov processes (see ...of Markov Chain Monte Carlo (MCMC) methods based on reversible Markov chains (see ...MCMC methods is to produce approximate ...
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Markov Chain Application in Object Oriented Software Designing
... interpretation-based Markov chain Geostatistical (MCG) framework for classifying land- use/land-cover (LULC) classes from remotely sensed ...finite Markov chains with unknown mean ...a ...
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Bayesian inference for a semi parametric copula based Markov chain
... In Finance, copula models have recently gained popularity, as they provide an alternative to the assumption of normality in data (see Embrechts et al. (1999)). Copula applications range from Value at Risk (VaR) analysis ...
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Salt Tectonism In The Carolina Trough
... supply chain. The two fundamental problems that arise from a poor supply chain are a large backlog and ...criticality based on their creation of backorders and surpluses can create significant ...
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Modeling link quality for high speed railway wireless networks based on hidden Markov chain
... In high-speed railway (HSR) wireless networks, the link quality is greatly time-dependent and location-varying. Due to the high randomness, it is challenging to predict the link quality in HSR wireless networks. In this ...
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Particle Gibbs with Ancestor Sampling
... and Markov chain Monte Carlo (MCMC, see, ...2001) methods in particular have found application to a wide range of data analysis problems involving complex, high-dimensional ...These methods ...
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A Markov-chain Activity-based Model for Pedestrians in Office Buildings
... While most previous studies have focussed on the operational behaviour level and have not been able to model complex situations for long periods of time, this paper present a tactical behaviour level model that is able ...
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Forecasting Of Probabilities of Iraqi Dinar Exchange Rate against (US) Dollar by Using Stochastic Process for the Period (2013-2014)
... random variables (for example, see Markov chain, also known as discrete-time Markov chain). The random variables corresponding to various times may be completely different, the only ...
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The Impact of Monetary Policy on Economic Growth in Cambodia: Bayesian Approach
... To ensure the stationary and convergence, we employ the diagnostic test through accessing many useful tests such as Geweke Test, Raftery and Lewis Test, Heidelberger and Welch Test which offer essential information about ...
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