scholarly journals Description of the Markov chain using the producing function

2021 ◽  
Vol 2131 (3) ◽  
pp. 032040
Author(s):  
T A Shornikova
Keyword(s):  
Markov Chain ◽  
Stochastic Model ◽  
Generating Function ◽  
Inverse Matrix ◽  
Characteristic Numbers ◽  
Secular Equations ◽  
Definition Of ◽  
Under Construction ◽  
Transitional Probabilities ◽  
Stationary Probabilities

Abstract In article the way of creation of an assumed function of transformation with the help of a generating function and also a method of use of characteristic numbers and vectors for creation of a matrix which elements well describe conditions of process at any moment is described. This approach differs from preceding that, having used a concept of characteristic numbers and characteristic vectors of a matrix of the transitional probabilities, it is possible to simplify considerably calculation of the elements characterizing process. In article methods of stochastic model operation, ways of the description of a generating function, the solution of matrixes of the equations by means of characteristic numbers and vectors are used. Using properties of a generating function, made “dictionary” of z-transformations which helped to define an assumed function of transformation. The generating function of a vector was applied to a research of behavior of a vector of absolute probabilities which elements represent stationary probabilities. For definition of degree of a matrix of transition of probabilities used a concept of characteristic numbers and characteristic vectors of the transitional probabilities. Determined by such way an unlimited set of latent vectors of which made matrixes which describe a condition of a system at any moment. Reception of definition of latent vectors in more difficult examples which is that along with required coefficients of secular equations the system of auxiliary matrixes and an inverse matrix is under construction is also described.

System of thermodynamic quantities in the McMillan-Mayer solution theory

1985 ◽  
Vol 50 (4) ◽  
pp. 791-798 ◽  
Author(s):  
Vilém Kodýtek
Keyword(s):  
Free Energy ◽  
Generating Function ◽  
Simple Form ◽  
Unit Volume ◽  
Thermodynamic Quantities ◽  
Solution Theory ◽  
Pure Solvent ◽  
Second Derivatives ◽  
Definition Of ◽  
Derivatives Of

The McMillan-Mayer (MM) free energy per unit volume of solution AMM, is employed as a generating function of the MM system of thermodynamic quantities for solutions in the state of osmotic equilibrium with pure solvent. This system can be defined by replacing the quantities G, T, P, and m in the definition of the Lewis-Randall (LR) system by AMM, T, P0, and c (P0 being the pure solvent pressure). Following this way the LR to MM conversion relations for the first derivatives of the free energy are obtained in a simple form. New relations are derived for its second derivatives.

On the absorption probabilities and mean time for absorption for discrete Markov chains

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Nikolaos Halidias
Keyword(s):  
Markov Chain ◽  
Random Walk ◽  
Markov Chains ◽  
Generating Function ◽  
Discrete Time ◽  
Probability Generating Function ◽  
The Mean ◽  
Mean Time ◽  
Absorption Probabilities

Abstract In this note we study the probability and the mean time for absorption for discrete time Markov chains. In particular, we are interested in estimating the mean time for absorption when absorption is not certain and connect it with some other known results. Computing a suitable probability generating function, we are able to estimate the mean time for absorption when absorption is not certain giving some applications concerning the random walk. Furthermore, we investigate the probability for a Markov chain to reach a set A before reach B generalizing this result for a sequence of sets A 1 , A 2 , … , A k {A_{1},A_{2},\dots,A_{k}} .

scholarly journals Proportional lumpability and proportional bisimilarity

2021 ◽  
Author(s):  
Andrea Marin ◽  
Carla Piazza ◽  
Sabina Rossi
Keyword(s):  
Markov Chain ◽  
Markov Chains ◽  
Upper And Lower Bounds ◽  
General Definition ◽  
Performance Indices ◽  
State Aggregation ◽  
Structural Regularity ◽  
Original Definition ◽  
Aggregation Technique ◽  
Definition Of

AbstractIn this paper, we deal with the lumpability approach to cope with the state space explosion problem inherent to the computation of the stationary performance indices of large stochastic models. The lumpability method is based on a state aggregation technique and applies to Markov chains exhibiting some structural regularity. Moreover, it allows one to efficiently compute the exact values of the stationary performance indices when the model is actually lumpable. The notion of quasi-lumpability is based on the idea that a Markov chain can be altered by relatively small perturbations of the transition rates in such a way that the new resulting Markov chain is lumpable. In this case, only upper and lower bounds on the performance indices can be derived. Here, we introduce a novel notion of quasi-lumpability, named proportional lumpability, which extends the original definition of lumpability but, differently from the general definition of quasi-lumpability, it allows one to derive exact stationary performance indices for the original process. We then introduce the notion of proportional bisimilarity for the terms of the performance process algebra PEPA. Proportional bisimilarity induces a proportional lumpability on the underlying continuous-time Markov chains. Finally, we prove some compositionality results and show the applicability of our theory through examples.

STOCHASTIC MODEL OF INFLOW TO RESERVOIR FOR MEDIUM-TERM HYDROLOGICAL FORECASTS WHEN FLOW REGULATING

2021 ◽  
pp. 124-131
Author(s):  
I. G. VELIEV ◽  
◽  
V. V. ILJINICH
Keyword(s):  
Water Management ◽  
Markov Chain ◽  
Stochastic Model ◽  
Decision Makers ◽  
Statistical Characteristics ◽  
Medium Term ◽  
Forecasting Methods ◽  
Balance Accuracy ◽  
Operational Management ◽  
Runoff Model

The article presents a stochastic model of runoff with a five-day discreteness within the water management years. The analysis performed regarding the main statistical characteristics of the inflow to the Krasnodar reservoir has allowed the conclusion that this model, based on a simple Markov chain, satisfies the balance accuracy of hydrological calculations for operational regulation of the runoff. The performed verification calculations have shown that the proposed method for obtaining medium-term runoff forecasts for 5 days, based on the developed stochastic runoff model, is satisfactory to the criteria of efficiency and accuracy of hydrological forecasting methods used in Russia. The specific example has shown that a stochastic runoff model can be useful to decision-makers regarding the operational management of a reservoir in real time.

Perturbation bounds for the stationary probabilities of a finite Markov chain

1984 ◽  
Vol 16 (04) ◽  
pp. 804-818 ◽  
Author(s):  
Moshe Haviv ◽  
Ludo Van Der Heyden
Keyword(s):  
Markov Chain ◽  
Stationary Distribution ◽  
Finite Markov Chain ◽  
Perturbation Bounds ◽  
Aggregation Technique ◽  
Stationary Probabilities

This paper discusses perturbation bounds for the stationary distribution of a finite indecomposable Markov chain. Existing bounds are reviewed. New bounds are presented which more completely exploit the stochastic features of the perturbation and which also are easily computable. Examples illustrate the tightness of the bounds and their application to bounding the error in the Simon–Ando aggregation technique for approximating the stationary distribution of a nearly completely decomposable Markov chain.

A carrier-borne epidemic with multiple stages of infection

1991 ◽  
Vol 28 (01) ◽  
pp. 1-8 ◽  
Author(s):  
J. Gani ◽  
Gy. Michaletzky
Keyword(s):  
Differential Equation ◽  
Partial Differential Equation ◽  
Markov Chain ◽  
Generating Function ◽  
Continuous Time ◽  
Probability Generating Function ◽  
Death Process ◽  
Partial Differential ◽  
Multiple Stages

This paper considers a carrier-borne epidemic in continuous time with m + 1 > 2 stages of infection. The carriers U(t) follow a pure death process, mixing homogeneously with susceptibles X 0(t), and infectives Xi (t) in stages 1≦i≦m of infection. The infectives progress through consecutive stages of infection after each contact with the carriers. It is shown that under certain conditions {X 0(t), X 1(t), · ··, Xm (t) U(t); t≧0} is an (m + 2)-variate Markov chain, and the partial differential equation for its probability generating function derived. This can be solved after a transfomation of variables, and the probability of survivors at the end of the epidemic found.

On the symplectically invariant variational principle and generating functions

1990 ◽  
Vol 431 (1883) ◽  
pp. 403-417 ◽  
Keyword(s):  
Quantum Mechanics ◽  
Variational Principle ◽  
Generating Function ◽  
Generating Functions ◽  
Geometric Approach ◽  
Canonical Transformations ◽  
Weyl Transform ◽  
Analogous Relation ◽  
Definition Of ◽  
Symplectic Invariance

The generating function for canonical transformations derived by Marinov has the important property of symplectic invariance (i. e. under linear canonical transformations). However, a more geometric approach to the rederivation of this function from the variational principle reveals that it is not free from caustic singularities after all. These singularities can be avoided without breaking the symplectic invariance by the definition of a complementary generating function bearing an analogous relation to the Woodward ambiguity function in telecommunications theory as that tying Marinov’s function to the Wigner function and the Weyl transform in quantum mechanics. Marinov’s function is specially apt to describe canonical transformations close to the identity, but breaks down for reflections through a point in phase space, easily described by the new generating function.

The censored Markov chain and the best augmentation

1996 ◽  
Vol 33 (03) ◽  
pp. 623-629 ◽  
Author(s):  
Y. Quennel Zhao ◽  
Danielle Liu
Keyword(s):  
Markov Chain ◽  
Probability Distribution ◽  
Transition Probability ◽  
Transition Probability Matrix ◽  
Stationary Probability ◽  
Stationary Probability Distribution ◽  
The Best Approximation ◽  
Finite Matrix ◽  
Countable State ◽  
Stationary Probabilities

Computationally, when we solve for the stationary probabilities for a countable-state Markov chain, the transition probability matrix of the Markov chain has to be truncated, in some way, into a finite matrix. Different augmentation methods might be valid such that the stationary probability distribution for the truncated Markov chain approaches that for the countable Markov chain as the truncation size gets large. In this paper, we prove that the censored (watched) Markov chain provides the best approximation in the sense that, for a given truncation size, the sum of errors is the minimum and show, by examples, that the method of augmenting the last column only is not always the best.

Study on Stochastic Model of Baseline Estimation of GPS Reference Station Network

2012 ◽  
Vol 170-173 ◽  
pp. 2785-2788
Author(s):  
Qiu Ying Guo ◽  
Guang Rong Hao ◽  
Tong Long Zhao
Keyword(s):  
Stochastic Model ◽  
Functional Model ◽  
Convergence Time ◽  
Reference Station ◽  
Observation Data ◽  
Station Network ◽  
Long Baseline ◽  
Calculation Results ◽  
Definition Of ◽  
Baseline Estimation

Baseline estimation is one of the most important links in the data processing of GPS reference station network. Exact definition of functional model and stochastic model of baseline estimation must be required to achieve high precise baseline solutions. The effects on precision of GPS long baseline estimation of three stochastic models are analyzed and compared by computation experiments using observation data of GPS reference station network. Calculation results show that using refined stochastic model can reduce convergence time of baseline solution. For baselines about 100km long in GPS reference station network, baseline precision of float and fixed solutions can be improved about 0.10m and 3mm respectively by satellite elevations compared with standard stochastic model using 10~40 minutes’ observation data and baseline precision of float and fixed solutions can be improved about 0.15m and 5mm respectively by estimated stochastic model based on theory of stationary stochastic process compared with standard stochastic model.

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