Analysis of Markov renewal shock models

1995 ◽  
Vol 32 (3) ◽  
pp. 821-831 ◽  
Author(s):  
Nobuko Igaki ◽  
Ushio Sumita ◽  
Masashi Kowada
Keyword(s):  
Stochastic Process ◽  
Markov Chain ◽  
System State ◽  
Random System ◽  
System Failures ◽  
Joint Distributions ◽  
Shock Models ◽  
Random Intervals ◽  
Similar Transform ◽  
Random Shocks

A trivariate stochastic process is considered, describing a sequence of random shocks {Xn} at random intervals {Yn} with random system state {Jn}. The triviariate stochastic process satisfies a Markov renewal property in that the magnitude of shocks and the shock intervals are correlated pairwise and the corresponding joint distributions are affected by transitions of the system state which occur after each shock according to a Markov chain. Of interest is a system lifetime terminated whenever a shock magnitude exceeds a prespecified level z. The distribution of system lifetime, its moments and a related exponential limit theorem are derived explicitly. A similar transform analysis is conducted for a second type of system lifetime with system failures caused by the cumulative magnitude of shocks exceeding a fixed level z.

Analysis of Markov renewal shock models

1995 ◽  
Vol 32 (03) ◽  
pp. 821-831 ◽  
Author(s):  
Nobuko Igaki ◽  
Ushio Sumita ◽  
Masashi Kowada
Keyword(s):  
Stochastic Process ◽  
Markov Chain ◽  
System State ◽  
Random System ◽  
System Failures ◽  
Joint Distributions ◽  
Shock Models ◽  
Random Intervals ◽  
Similar Transform ◽  
Random Shocks

A trivariate stochastic process is considered, describing a sequence of random shocks {Xn } at random intervals {Y n} with random system state {Jn }. The triviariate stochastic process satisfies a Markov renewal property in that the magnitude of shocks and the shock intervals are correlated pairwise and the corresponding joint distributions are affected by transitions of the system state which occur after each shock according to a Markov chain. Of interest is a system lifetime terminated whenever a shock magnitude exceeds a prespecified level z. The distribution of system lifetime, its moments and a related exponential limit theorem are derived explicitly. A similar transform analysis is conducted for a second type of system lifetime with system failures caused by the cumulative magnitude of shocks exceeding a fixed level z.

scholarly journals Chain-referral sampling on stochastic block models

2020 ◽  
Vol 24 ◽  
pp. 718-738
Author(s):  
Thi Phuong Thuy Vo
Keyword(s):  
Stochastic Process ◽  
Markov Chain ◽  
Social Network ◽  
Discrete Time ◽  
Precise Description ◽  
Stochastic Block Model ◽  
Hidden Population ◽  
The People ◽  
A Chain ◽  
Block Models

The discovery of the “hidden population”, whose size and membership are unknown, is made possible by assuming that its members are connected in a social network by their relationships. We explore these groups by a chain-referral sampling (CRS) method, where participants recommend the people they know. This leads to the study of a Markov chain on a random graph where vertices represent individuals and edges connecting any two nodes describe the relationships between corresponding people. We are interested in the study of CRS process on the stochastic block model (SBM), which extends the well-known Erdös-Rényi graphs to populations partitioned into communities. The SBM considered here is characterized by a number of vertices N, a number of communities (blocks) m, proportion of each community π = (π1, …, πm) and a pattern for connection between blocks P = (λkl∕N)(k,l)∈{1,…,m}2. In this paper, we give a precise description of the dynamic of CRS process in discrete time on an SBM. The difficulty lies in handling the heterogeneity of the graph. We prove that when the population’s size is large, the normalized stochastic process of the referral chain behaves like a deterministic curve which is the unique solution of a system of ODEs.

Criteria for the non-ergodicity of stochastic processes: application to the exponential back-off protocol

1987 ◽  
Vol 24 (02) ◽  
pp. 347-354 ◽  
Author(s):  
Guy Fayolle ◽  
Rudolph Iasnogorodski
Keyword(s):  
Stochastic Process ◽  
Markov Chain ◽  
Stochastic Processes ◽  
State Space ◽  
Discrete Time ◽  
Random Variables ◽  
Countable State Space ◽  
Countable State ◽  
New Criteria

In this paper, we present some simple new criteria for the non-ergodicity of a stochastic process (Yn ), n ≧ 0 in discrete time, when either the upward or downward jumps are majorized by i.i.d. random variables. This situation is encountered in many practical situations, where the (Yn ) are functionals of some Markov chain with countable state space. An application to the exponential back-off protocol is described.

scholarly journals Approximations: replacing random variables with their means

2014 ◽  
Vol 51 (A) ◽  
pp. 57-62
Author(s):  
Joe Gani
Keyword(s):  
Stochastic Process ◽  
Markov Chain ◽  
Continuous Time ◽  
Random Variables ◽  
Original Form ◽  
Continuous Time Markov Chain ◽  
Standard Methods ◽  
Sis Epidemics

One of the standard methods for approximating a bivariate continuous-time Markov chain {X(t), Y(t): t ≥ 0}, which proves too difficult to solve in its original form, is to replace one of its variables by its mean, This leads to a simplified stochastic process for the remaining variable which can usually be solved, although the technique is not always optimal. In this note we consider two cases where the method is successful for carrier infections and mutating bacteria, and one case where it is somewhat less so for the SIS epidemics.

Reliability assessment of system under a generalized cumulative shock model

2019 ◽  
Vol 234 (1) ◽  
pp. 129-137 ◽  
Author(s):  
Min Gong ◽  
Serkan Eryilmaz ◽  
Min Xie
Keyword(s):  
Reliability Assessment ◽  
Shock Model ◽  
External Shocks ◽  
Phase Type Distribution ◽  
Internal Factors ◽  
Reliability Characteristics ◽  
Shock Models ◽  
The One ◽  
Random Shocks ◽  
Real Practice

Reliability assessment of system suffering from random shocks is attracting a great deal of attention in recent years. Excluding internal factors such as aging and wear-out, external shocks which lead to sudden changes in the system operation environment are also important causes of system failure. Therefore, efficiently modeling the reliability of such system is an important applied problem. A variety of shock models are developed to model the inter-arrival time between shocks and magnitude of shocks. In a cumulative shock model, the system fails when the cumulative magnitude of damage caused by shocks exceed a threshold. Nevertheless, in the existing literatures, only the magnitude is taken into consideration, while the source of shocks is usually neglected. Using the same distribution to model the magnitude of shocks from different sources is too critical in real practice. To this end, considering a system subject to random shocks from various sources with different probabilities, we develop a generalized cumulative shock model in this article. We use phase-type distribution to model the variables, which is highly versatile to be used for modeling quantitative features of random phenomenon. We will discuss the reliability characteristics of such system in some detail and give some clear expressions under the one-dimensional case. Numerical example for illustration is also provided along with a summary.

scholarly journals Prediction of Status Particulate Matter 2.5 Using State Markov Chain Stochastic Process and HYBRID VAR-NN-PSO

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 161654-161665 ◽  
Author(s):  
Rezzy Eko Caraka ◽  
Rung Ching Chen ◽  
Toni Toharudin ◽  
Bens Pardamean ◽  
Hasbi Yasin ◽  
...  
Keyword(s):  
Stochastic Process ◽  
Markov Chain ◽  
Particulate Matter ◽  
Particulate Matter 2.5

Lattice Free Stochastic Dynamics

2012 ◽  
Vol 12 (3) ◽  
pp. 691-702 ◽  
Author(s):  
Alexandros Sopasakis
Keyword(s):  
Stochastic Process ◽  
Monte Carlo ◽  
Markov Chain ◽  
Hard Sphere ◽  
Stochastic Dynamics ◽  
Particle System ◽  
Kinetic Monte Carlo ◽  
Packing Problem ◽  
Kinetic Monte Carlo Method ◽  
Free Environment

AbstractWe introduce a lattice-free hard sphere exclusion stochastic process. The resulting stochastic rates are distance based instead of cell based. The corresponding Markov chain build for this many particle system is updated using an adaptation of the kinetic Monte Carlo method. It becomes quickly apparent that due to the lattice-free environment, and because of that alone, the dynamics behave differently than those in the lattice-based environment. This difference becomes increasingly larger with respect to particle densities/temperatures. The well-known packing problem and its solution (Palasti conjecture) seem to validate the resulting lattice-free dynamics.

scholarly journals Hitting Time and Inverse Problems for Markov Chains

2008 ◽  
Vol 45 (03) ◽  
pp. 640-649
Author(s):  
Victor de la Peña ◽  
Henryk Gzyl ◽  
Patrick McDonald
Keyword(s):  
Markov Chain ◽  
Inverse Problems ◽  
Markov Chains ◽  
Transition Probabilities ◽  
Hitting Time ◽  
First Hitting Time ◽  
Joint Distributions ◽  
Finite Set

Let W n be a simple Markov chain on the integers. Suppose that X n is a simple Markov chain on the integers whose transition probabilities coincide with those of W n off a finite set. We prove that there is an M > 0 such that the Markov chain W n and the joint distributions of the first hitting time and first hitting place of X n started at the origin for the sets {-M, M} and {-(M + 1), (M + 1)} algorithmically determine the transition probabilities of X n .

A stochastic process for modeling failures of a system having a non-monotonic hazard rate function

2019 ◽  
Vol 233 (5) ◽  
pp. 731-746
Author(s):  
Lucianne Varn ◽  
Stefanka Chukova ◽  
Richard Arnold
Keyword(s):  
Stochastic Process ◽  
Hazard Rate ◽  
Repair Process ◽  
Rate Function ◽  
Hazard Rates ◽  
Failure Rates ◽  
Hazard Rate Function ◽  
Repairable Systems ◽  
System Failures ◽  
Quantities Of Interest

Reliability literature on modeling failures of repairable systems mostly deals with systems having monotonically increasing hazard/failure rates. When the hazard rate of a system is non-monotonic, models developed for monotonically increasing failure rates cannot be effectively applied without making assumptions on the types of repair performed following system failures. For instance, for systems having bathtub-shaped hazard rates, it is assumed that during the initial, decreasing hazard rate phase, all repairs are minimal. These assumptions on the type of general repair can be restrictive. In order to relax these assumptions, it has been suggested that general repairs in the initially decreasing phase can be modeled as “aging” the system. This approach however does not preserve the order of effectiveness of the types of general repair as defined in the literature. In this article, we develop a set of models to address these shortcomings. We propose a new stochastic process to model consecutive failures of repairable systems having non-monotonic, specifically bathtub-shaped, hazard rates, where the types of general repair are not restricted and the order of the effectiveness of the types of repair is preserved. The proposed models guarantee that a repaired system is at least as reliable as one that has not failed (or equivalently one that has been minimally repaired). To illustrate the models, we present multiple examples and simulate the failure-repair process and estimate the quantities of interest.

Revealing a temporal symmetry/asymmetry dichotomy in a Markovian setting, and a parameterization based on fractional low-order joint moments

2020 ◽  
Author(s):  
Alin Andrei Carsteanu ◽  
Andreas Langousis
Keyword(s):  
Stochastic Process ◽  
Probability Distributions ◽  
Arrow Of Time ◽  
Marginal Probability ◽  
Joint Moments ◽  
Statistical Tool ◽  
Joint Distributions ◽  
Striking Result ◽  
Temporal Symmetry ◽  
Low Order

<p>We show that "an arrow of time", which is reflected by the joint distributions of successive variables in a stochastic process, may exist (or not) solely on grounds of marginal probability distributions, without affecting stationarity or involving the structural dependencies within the process. The temporal symmetry/asymmetry dichotomy thus revealed, is exemplified for the simplest case of stably-distributed Markovian recursions, where the lack of Gaussianity, even when the increments of the process are independent and identically distributed (i.i.d.) with symmetric marginal, is generating a break of temporal symmetry. We devise a statistical tool to evidence this striking result, based on fractional low-order joint moments, whose existence is guaranteed even for the case of "fat-tailed" strictly-stable distributions, and is thereby suited for parameterizing structural dependencies within such a process.</p>

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