# Markov model

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## Markov model

(probability, simulation)
A model or simulation based on Markov chains.
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Based on the existing models predicting periodical building energy use, and research on Markov Chain models handling stochastic phenomenon, a Markov Chain based model for projecting lifecycle energy consumption is proposed in the paper to address the longitudinal uncertainties in annual energy consumption, building's service life and the corresponding linear-average algorithm.
ArrayList Q = null; //the observation sequence int R = 0; //the order of the Markov Chain int N = 0; //number of observation symbols int T = 0; //length of observation sequence int context []; //prediction context public Markov (int nObservationSymbols, int order) { R = order; //the order of the Markov Chain context = new int [R]; N = nObservationSymbols; //the number of distinct observation symbols Q = new java.
Fatigue curve approximation using Daniels' sequence and Markov chains, in Material Digest of the XXIV Scientific and Practical Conference "Theory and Practice in Physical, Mathematical and Technical Sciences" London: IASHE, 31-33.
Anderson TW, Goodman LA (1957) Statistical inference about Markov chains.
This straightforward argument applies verbatim to lump Hopf-power Markov chains on other graded Hopf algebras.
Nearly uncoupled Markov chains are very much related to a graph theoretic concept known as clustering.
Markov chain is a stochastic process that handles the uncertainty condition of road system performance through time.
A Markov chain defined on different payment states of a mortgage loan allows one to define and calculate a health index on the loan portfolio which can be used as a performance measure of that portfolio.
n], n [greater than or equal to] 0} be an mth- order nonhomogeneous Markov chain with the m dimensional initial distribution (31) and the mth-order transition matrices (33).
1962) Estimation of the allowance for doubtful accounts by Markov chains, Management Science, 8(3), pp.
A general approach to solving problems of operating process management that can be defined by Markov chains is comprised of examining k of different rules (strategies) leading to appropriate solutions.
In the analysis, use is made of standard methods, as developed by Anderson and Goodman (1957) (and applied by Bhargava, 1962; and Fielitz and Bhargava, 1973) for drawing statistical inferences in time when Markov Chains are applied.

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