Iterative Bounds on the Equilibrium Distribution of a Finite Markov Chain

1987 ◽  
Vol 1 (1) ◽  
pp. 117-131 ◽  
Author(s):  
Jan Van Der Wal ◽  
Paul J. Schweitzer
Keyword(s):  
Markov Chain ◽  
Iterative Method ◽  
Lower Bounds ◽  
Equilibrium Distribution ◽  
Upper And Lower Bounds ◽  
Finite Markov Chain ◽  
Expected Number ◽  
Numerical Examples ◽  
Number Of Visits ◽  
New Iterative Method

This article presents a new iterative method for computing the equilibrium distribution of a finite Markov chain, which has the significant advantage of providing good upper and lower bounds for the equilibrium probabilities. The method approximates the expected number of visits to each state between two successive visits to a given reference state. Numerical examples indicate that the performance of this method is quite good.

On the two sided barrier problem

1965 ◽  
Vol 2 (01) ◽  
pp. 79-87
Author(s):  
Masanobu Shinozuka
Keyword(s):  
Random Process ◽  
Ground Motion ◽  
Lower Bounds ◽  
Present Method ◽  
Structural Response ◽  
Random Processes ◽  
Upper And Lower Bounds ◽  
Numerical Examples ◽  
Long Run ◽  
Barrier Problem

Upper and lower bounds are given for the probability that a separable random process X(t) will take values outside the interval (— λ 1, λ 2) for 0 ≦ t ≦ T, where λ 1 and λ 2 are positive constants. The random process needs to be neither stationary, Gaussian nor purely random (white noise). In engineering applications, X(t) is usually a random process decaying with time at least in the long run such as the structural response to the acceleration of ground motion due to earthquake. Numerical examples show that the present method estimates the probability between the upper and lower bounds which are sufficiently close to be useful when the random processes decay with time.

The inverse problem in reducible Markov chains

1976 ◽  
Vol 13 (01) ◽  
pp. 49-56 ◽  
Author(s):  
W. D. Ray ◽  
F. Margo
Keyword(s):  
Inverse Problem ◽  
Markov Chain ◽  
Equilibrium Distribution ◽  
Probability Distributions ◽  
A Priori ◽  
Feasible Region ◽  
Numerical Examples ◽  
Transient States ◽  
Equilibrium Probability ◽  
Absorbing States

The equilibrium probability distribution over the set of absorbing states of a reducible Markov chain is specified a priori and it is required to obtain the constrained sub-space or feasible region for all possible initial probability distributions over the set of transient states. This is called the inverse problem. It is shown that a feasible region exists for the choice of equilibrium distribution. Two different cases are studied: Case I, where the number of transient states exceeds that of the absorbing states and Case II, the converse. The approach is via the use of generalised inverses and numerical examples are given.

The accuracy of Rayleigh’s method of calculating the natural frequencies of vibrating systems

1952 ◽  
Vol 211 (1105) ◽  
pp. 204-224 ◽  
Keyword(s):  
Lower Bounds ◽  
Natural Frequencies ◽  
Hermitian Operator ◽  
Normal Modes ◽  
Comparison Theorems ◽  
Upper And Lower Bounds ◽  
Subsequent Paper ◽  
Numerical Examples ◽  
Modes Of Vibration ◽  
Vibrating Systems

In this paper a theorem of Kato (1949) which provides upper and lower bounds for the eigenvalues of a Hermitian operator is modified and generalized so as to give upper and lower bounds for the normal frequencies of oscillation of a conservative dynamical system. The method given here is directly applicable to a system specified by generalized co-ordinates with both elastic and inertial couplings. It can be applied to any one of the normal modes of vibration of the system. The bounds obtained are much closer than those given by Rayleigh’s comparison theorems in which the inertia or elasticity of the system is changed, and they are in fact the ‘best possible’ bounds. The principles of the computation of upper and lower bounds is explained in this paper and will be illustrated by some numerical examples in a subsequent paper.

scholarly journals A New Iterative Method for Finding Approximate Inverses of Complex Matrices

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
M. Kafaei Razavi ◽  
A. Kerayechian ◽  
M. Gachpazan ◽  
S. Shateyi
Keyword(s):  
Iterative Method ◽  
Order Of Convergence ◽  
Complex Matrices ◽  
Approximate Inverse ◽  
Numerical Examples ◽  
Convergence Behavior ◽  
Approximate Inverses ◽  
Nonsingular Matrices ◽  
New Iterative Method

This paper presents a new iterative method for computing the approximate inverse of nonsingular matrices. The analytical discussion of the method is included to demonstrate its convergence behavior. As a matter of fact, it is proven that the suggested scheme possesses tenth order of convergence. Finally, its performance is illustrated by numerical examples on different matrices.

scholarly journals Bounds for spectral radius of nonnegative tensors using matrix-digragh-based approach

2021 ◽  
Vol 0 (0) ◽  
pp. 0
Author(s):  
Guimin Liu ◽  
Hongbin Lv
Keyword(s):  
Spectral Radius ◽  
Lower Bounds ◽  
Upper And Lower Bounds ◽  
Nonnegative Tensor ◽  
Related Literature ◽  
Numerical Examples ◽  
Nonnegative Tensors

<p style='text-indent:20px;'>We obtain the improved results of the upper and lower bounds for the spectral radius of a nonnegative tensor by its majorization matrix's digraph. Numerical examples are also given to show that our results are significantly superior to the results of related literature.</p>

On the two sided barrier problem

1965 ◽  
Vol 2 (1) ◽  
pp. 79-87 ◽  
Author(s):  
Masanobu Shinozuka
Keyword(s):  
Random Process ◽  
Ground Motion ◽  
Lower Bounds ◽  
Present Method ◽  
Structural Response ◽  
Random Processes ◽  
Upper And Lower Bounds ◽  
Numerical Examples ◽  
Long Run ◽  
Barrier Problem

Upper and lower bounds are given for the probability that a separable random process X(t) will take values outside the interval (— λ1, λ2) for 0 ≦ t ≦ T, where λ1 and λ2 are positive constants.The random process needs to be neither stationary, Gaussian nor purely random (white noise).In engineering applications, X(t) is usually a random process decaying with time at least in the long run such as the structural response to the acceleration of ground motion due to earthquake.Numerical examples show that the present method estimates the probability between the upper and lower bounds which are sufficiently close to be useful when the random processes decay with time.

scholarly journals SERIES EXPANSIONS FOR FINITE-STATE MARKOV CHAINS

2007 ◽  
Vol 21 (3) ◽  
pp. 381-400 ◽  
Author(s):  
Bernd Heidergott ◽  
Arie Hordijk ◽  
Miranda van Uitert
Keyword(s):  
Markov Chain ◽  
Markov Chains ◽  
Stationary Distribution ◽  
Numerical Algorithm ◽  
Series Expansions ◽  
Finite Markov Chain ◽  
Numerical Examples ◽  
Finite State

This article provides series expansions of the stationary distribution of a finite Markov chain. This leads to an efficient numerical algorithm for computing the stationary distribution of a finite Markov chain. Numerical examples are given to illustrate the performance of the algorithm.

scholarly journals Some Refinements and Generalizations of I. Schur Type Inequalities

2014 ◽  
Vol 2014 ◽  
pp. 1-8
Author(s):  
Xian-Ming Gu ◽  
Ting-Zhu Huang ◽  
Wei-Ru Xu ◽  
Hou-Biao Li ◽  
Liang Li ◽  
...  
Keyword(s):  
Lower Bounds ◽  
Recent Literature ◽  
Structural Characteristics ◽  
Variable Parameter ◽  
Upper Bounds ◽  
Upper And Lower Bounds ◽  
Numerical Examples ◽  
Schur Inequality

Recently, extensive researches on estimating the value ofehave been studied. In this paper, the structural characteristics of I. Schur type inequalities are exploited to generalize the corresponding inequalities by variable parameter techniques. Some novel upper and lower bounds for the I. Schur inequality have also been obtained and the upper bounds may be obtained with the help ofMapleand automated proving package (Bottema). Numerical examples are employed to demonstrate the reliability of the approximation of these new upper and lower bounds, which improve some known results in the recent literature.

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