Studies on the specificity of HIV protease: An application of Markov chain theory

1993 ◽  
Vol 12 (6) ◽  
pp. 709-724 ◽  
Author(s):  
Kuo-Chen Chou ◽  
Chun-Ting Zhang
Keyword(s):  
Markov Chain ◽  
Hiv Protease ◽  
Markov Chain Theory ◽  
Chain Theory

scholarly journals Generation method for the PV power time series combining the decomposition technique and Markov chain theory

2017 ◽  
Vol 2017 (13) ◽  
pp. 2026-2031
Author(s):  
Shenzhi Xu ◽  
Xiaomeng Ai ◽  
Jiakun Fang ◽  
Jinyu Wen ◽  
Pai Li ◽  
...  
Keyword(s):  
Time Series ◽  
Markov Chain ◽  
Decomposition Technique ◽  
Markov Chain Theory ◽  
Chain Theory

scholarly journals An ergodic algorithm for generating knots with a prescribed injectivity radius

2019 ◽  
Vol 28 (06) ◽  
pp. 1950045
Author(s):  
Kyle Leland Chapman
Keyword(s):  
Markov Chain ◽  
Lattice Model ◽  
Probability Distributions ◽  
Positive Probability ◽  
Bending Angle ◽  
Markov Chain Theory ◽  
Countable Space ◽  
Chain Theory ◽  
Random Reflections ◽  
Planar Polygon

The first provably ergodic algorithm for sampling the space of thick equilateral knots off-lattice, as a function of thickness, will be described. This algorithm is based on previous algorithms of applying random reflections. It is an off-lattice generalization of the pivot algorithm. This move to an off-lattice model provides a huge improvement in power and efficacy in that samples can have arbitrary values for parameters such as the thickness constraint, bending angle, and torsion, while the lattice forces these parameters into a small number of specific values. This benefit requires working in a manifold rather than a finite or countable space, which forces the use of more novel methods in Markov–Chain theory. To prove the validity of the algorithm, we describe a method for turning any knot into the regular planar polygon using only thickness non-decreasing moves. This approach ensures that the algorithm has a positive probability of connecting any two knots with the required thickness constraint which is used to show that the algorithm is ergodic. This ergodic sampling allows for a statistically valid method for estimating probability distributions of arbitrary functions on the space of thick knots.

Markov Chain Models of Grinding Profiles

1964 ◽  
Vol 86 (4) ◽  
pp. 383-387 ◽  
Author(s):  
H. T. McAdams
Keyword(s):  
Markov Chain ◽  
Stochastic Model ◽  
Recurrent Event ◽  
Grinding Process ◽  
Kolmogorov Equations ◽  
First Order ◽  
Markov Chain Models ◽  
Event Theory ◽  
Markov Chain Theory ◽  
Chain Theory

Profiles of abrasive surfaces are analyzed by means of Markov chain theory. The Chapman-Kolmogorov equations, together with recurrent-event theory, are used to deduce theoretical distributions for such important statistics as the distances between effective cutting points and the lengths of lands on a worn grinding surface. Both first-order and second-order Markov chains are examined for their applicability to a stochastic model of the grinding process.

Towards consensus: some convergence theorems on repeated averaging

1977 ◽  
Vol 14 (01) ◽  
pp. 89-97 ◽  
Author(s):  
S. Chatterjee ◽  
E. Seneta
Keyword(s):  
Markov Chain ◽  
Consensus Problem ◽  
Stochastic Matrices ◽  
Convergence Theorems ◽  
Strong Ergodicity ◽  
Markov Chain Theory ◽  
Inhomogeneous Markov Chain ◽  
Chain Theory ◽  
Equivalent Conditions

The problem of tendency to consensus in an information-exchanging operation is connected with the ergodicity problem for backwards products of stochastic matrices. For such products, weak and strong ergodicity, defined analogously to these concepts for forward products of inhomogeneous Markov chain theory, are shown (in contrast to that theory) to be equivalent. Conditions for ergodicity are derived and their relation to the consensus problem is considered.

Stationary joint distributions arising in the analysis of the M/G/1 queue by the method of the imbedded markov chain

1966 ◽  
Vol 3 (2) ◽  
pp. 512-520 ◽  
Author(s):  
J. H. Jenkins
Keyword(s):  
Markov Chain ◽  
Generating Functions ◽  
Joint Distribution ◽  
Marginal Distribution ◽  
Probability Generating Function ◽  
Joint Distributions ◽  
Left Behind ◽  
Markov Chain Theory ◽  
Imbedded Markov Chain ◽  
Chain Theory

SummaryProbability generating functions are used to relate the joint distribution of the numbers of customers left behind by two successive departing customers to the marginal distribution of the number left behind by each departing customer. A probability generating function is then found for the joint distribution of the numbers of customers arriving in two successive departure intervals using the joint distribution of the numbers of customers left behind by three successive departing customers. The results could be obtained from general Markov chain theory but the method used in this paper is quicker.

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