Explicit formulae for stationary distributions of stress release processes

2000 ◽  
Vol 37 (2) ◽  
pp. 315-321 ◽  
Author(s):  
K. Borovkov ◽  
D. Vere-Jones
Keyword(s):  
Closed Form ◽  
Stationary Distribution ◽  
Exponential Function ◽  
Markov Models ◽  
Risk Process ◽  
Stress Release ◽  
Stationary Distributions ◽  
Explicit Formulae ◽  
Tectonic Forces ◽  
Release Processes

Stress release processes are special Markov models attempting to describe the behaviour of stress and occurrence of earthquakes in seismic zones. The stress is built up linearly by tectonic forces and released spontaneously when earthquakes occur. Assuming that the risk is an exponential function of the stress, we derive closed form expressions for the stationary distribution of such processes, the moments of the risk, and the autocovariance function of the reciprocal risk process.

Explicit formulae for stationary distributions of stress release processes

2000 ◽  
Vol 37 (02) ◽  
pp. 315-321 ◽  
Author(s):  
K. Borovkov ◽  
D. Vere-Jones
Keyword(s):  
Closed Form ◽  
Stationary Distribution ◽  
Exponential Function ◽  
Markov Models ◽  
Risk Process ◽  
Stress Release ◽  
Stationary Distributions ◽  
Explicit Formulae ◽  
Tectonic Forces ◽  
Release Processes

Stress release processes are special Markov models attempting to describe the behaviour of stress and occurrence of earthquakes in seismic zones. The stress is built up linearly by tectonic forces and released spontaneously when earthquakes occur. Assuming that the risk is an exponential function of the stress, we derive closed form expressions for the stationary distribution of such processes, the moments of the risk, and the autocovariance function of the reciprocal risk process.

scholarly journals Distributions stationnaires d'un système bonus–malus et probabilité de ruine

1988 ◽  
Vol 18 (1) ◽  
pp. 31-46 ◽  
Author(s):  
Par François Dufresne
Keyword(s):  
Theoretical Model ◽  
Stationary Distribution ◽  
Recursive Algorithm ◽  
Third Party ◽  
Ruin Probabilities ◽  
Stationary Distributions ◽  
Probability Of Ruin

AbstractIt is shown how the stationary distributions of a bonus–malus system can be computed recursively. It is further shown that there is an intrinsic relationship between such a stationary distribution and the probability of ruin in the risk-theoretical model. The recursive algorithm is applied to the Swiss bonus–malus system for automobile third-party liability and can be used to evaluate ruin probabilities.

scholarly journals Basing population genetic inferences and models of molecular evolution upon desired stationary distributions of DNA or protein sequences

2008 ◽  
Vol 363 (1512) ◽  
pp. 3931-3939 ◽  
Author(s):  
Sang Chul Choi ◽  
Benjamin D Redelings ◽  
Jeffrey L Thorne
Keyword(s):  
Molecular Evolution ◽  
Markov Model ◽  
Stationary Distribution ◽  
Population Genetic ◽  
Amino Acid Level ◽  
Protein Sequences ◽  
Relative Fitness ◽  
Evolutionary Models ◽  
Effective Population ◽  
Stationary Distributions

Models of molecular evolution tend to be overly simplistic caricatures of biology that are prone to assigning high probabilities to biologically implausible DNA or protein sequences. Here, we explore how to construct time-reversible evolutionary models that yield stationary distributions of sequences that match given target distributions. By adopting comparatively realistic target distributions, evolutionary models can be improved. Instead of focusing on estimating parameters, we concentrate on the population genetic implications of these models. Specifically, we obtain estimates of the product of effective population size and relative fitness difference of alleles. The approach is illustrated with two applications to protein-coding DNA. In the first, a codon-based evolutionary model yields a stationary distribution of sequences, which, when the sequences are translated, matches a variable-length Markov model trained on human proteins. In the second, we introduce an insertion–deletion model that describes selectively neutral evolutionary changes to DNA. We then show how to modify the neutral model so that its stationary distribution at the amino acid level can match a profile hidden Markov model, such as the one associated with the Pfam database.

Stationary distributions for dams with additive input and content-dependent release rate

1977 ◽  
Vol 9 (03) ◽  
pp. 645-663 ◽  
Author(s):  
P. J. Brockwell
Keyword(s):  
Continuous Function ◽  
Probability Measure ◽  
Stationary Distribution ◽  
Release Rate ◽  
Zero Set ◽  
Stationary Distributions ◽  
Special Cases ◽  
Necessary And Sufficient ◽  
Limiting Case ◽  
Finite Dam

Conditions are derived under which a probability measure on the Borel subsets of [0, ∞) is a stationary distribution for the content {Xt } of an infinite dam whose cumulative input {At } is a pure-jump Lévy process and whose release rate is a non-decreasing continuous function r(·) of the content. The conditions are used to find stationary distributions in a number of special cases, in particular when and when r(x) = x α and {A t } is stable with index β ∊ (0, 1). In general if EAt , < ∞ and r(0 +) > 0 it is shown that the condition sup r(x)>EA 1 is necessary and sufficient for a stationary distribution to exist, a stationary distribution being found explicitly when the conditions are satisfied. If sup r(x)>EA 1 it is shown that there is at most one stationary distribution and that if there is one then it is the limiting distribution of {Xt } as t → ∞. For {At } stable with index β and r(x) = x α , α + β = 1, we show also that complementing results of Brockwell and Chung for the zero-set of {Xt } in the cases α + β < 1 and α + β > 1. We conclude with a brief treatment of the finite dam, regarded as a limiting case of infinite dams with suitably chosen release functions.

scholarly journals On Truncation of the Matrix-Geometric Stationary Distributions

Mathematics ◽  
2019 ◽  
Vol 7 (9) ◽  
pp. 798 ◽  
Author(s):  
Naumov ◽  
Gaidamaka ◽  
Samouylov
Keyword(s):  
Finite Number ◽  
Stationary Distribution ◽  
Queueing Systems ◽  
Death Process ◽  
Birth And Death Process ◽  
Loss System ◽  
Stationary Distributions ◽  
Open Network ◽  
The Matrix

In this paper, we study queueing systems with an infinite and finite number of waiting places that can be modeled by a Quasi-Birth-and-Death process. We derive the conditions under which the stationary distribution for a loss system is a truncation of the stationary distribution of the Quasi-Birth-and-Death process and obtain the stationary distributions of both processes. We apply the obtained results to the analysis of a semi-open network in which a customer from an external queue replaces a customer leaving the system at the node from which the latter departed.

Level crossings and stationary distributions for general dams

1980 ◽  
Vol 17 (1) ◽  
pp. 218-226 ◽  
Author(s):  
Michael Rubinovitch ◽  
J. W. Cohen
Keyword(s):  
Explicit Expression ◽  
Stationary Distribution ◽  
Basic Relation ◽  
Expected Number ◽  
Stationary Distributions ◽  
Level Crossings ◽  
Fixed Level

Level crossings in a stationary dam process with additive input and arbitrary release are considered and an explicit expression for the expected number of downcrossings (and also overcrossings) of a fixed level, per time unit, is obtained. This leads to a short derivation of a basic relation which the stationary distribution of a general dam must satisfy.

Quasi-stationary distributions and one-dimensional circuit-switched networks

1987 ◽  
Vol 24 (04) ◽  
pp. 965-977 ◽  
Author(s):  
Ilze Ziedins
Keyword(s):  
Communication Networks ◽  
Stationary Distribution ◽  
Death Process ◽  
Birth And Death Process ◽  
Stationary Distributions ◽  
Switched Networks ◽  
One Dimensional ◽  
Special Cases ◽  
Quasi Stationary Distribution

We discuss the quasi-stationary distribution obtained when a simple birth and death process is conditioned on never exceeding K. An application of this model to one-dimensional circuit-switched communication networks is described, and some special cases examined.

scholarly journals On explicit form of the stationary distributions for a class of bounded Markov chains

2016 ◽  
Vol 53 (1) ◽  
pp. 231-243 ◽  
Author(s):  
S. McKinlay ◽  
K. Borovkov
Keyword(s):  
Markov Chains ◽  
Closed Form ◽  
State Space ◽  
Explicit Form ◽  
Discrete Time ◽  
Unit Interval ◽  
Time Step ◽  
Stationary Distributions ◽  
End Point ◽  
Current Location

AbstractWe consider a class of discrete-time Markov chains with state space [0, 1] and the following dynamics. At each time step, first the direction of the next transition is chosen at random with probability depending on the current location. Then the length of the jump is chosen independently as a random proportion of the distance to the respective end point of the unit interval, the distributions of the proportions being fixed for each of the two directions. Chains of that kind were the subjects of a number of studies and are of interest for some applications. Under simple broad conditions, we establish the ergodicity of such Markov chains and then derive closed-form expressions for the stationary densities of the chains when the proportions are beta distributed with the first parameter equal to 1. Examples demonstrating the range of stationary distributions for processes described by this model are given, and an application to a robot coverage algorithm is discussed.

scholarly journals Recursive Deviance Information Criterion for the Hidden Markov Model

2015 ◽  
Vol 5 (1) ◽  
pp. 61
Author(s):  
Safaa K. Kadhem ◽  
Paul Hewson ◽  
Irene Kaimi
Keyword(s):  
Closed Form ◽  
Markov Models ◽  
Likelihood Function ◽  
Hidden Markov ◽  
Deviance Information Criterion ◽  
Synthetic Data ◽  
Information Criterion ◽  
Conditional Likelihood ◽  
Main Challenge ◽  
Hidden States

In Bayesian model selection, the deviance information criterion (DIC) has become a widely used criterion. It is however not defined for the hidden Markov models (HMMs). In particular, the main challenge of applying the DIC for HMMs is that the observed likelihood function of such models is not available in closed form. A closed form for the observed likelihood function can be obtained either by summing all possible hidden states of the complete likelihood using the so-called the forward recursion, or via integrating out the hidden states in the conditional likelihood. Hence, we propose two versions of the DIC to the model choice problem in HMMs context, namely, the recursive deviance-based DIC and the conditional likelihood-based DIC. In this paper, we compare several normal HMMs after they are estimated by Bayesian MCMC method. We conduct a simulation study based on synthetic data generated under two assumptions, namely diversity in the heterogeneity level and also the number of states. We show that the recursive deviance-based DIC performs well in selecting the correct model compared with the conditional likelihood-based DIC that prefers the more complicated models. A real application involving the waiting time of Old Faithful Geyser data was also used to check those criteria. All the simulations were conducted in Python v.2.7.10, available from first author on request.

On the stationary distribution of some extremal Markovian sequences

1990 ◽  
Vol 27 (2) ◽  
pp. 291-302 ◽  
Author(s):  
M. T. Alpuim ◽  
E. Athayde
Keyword(s):  
Stationary Distribution ◽  
Beta Distribution ◽  
Random Variables ◽  
Random Variable ◽  
Stationary Distributions

This paper is concerned with the Markovian sequence Xn = Zn max{Xn–1, Yn},n ≧ 1, where X0 is any random variable, {Zn} and {Yn} are independent sequences of i.i.d. random variables both independent of X0. We consider the problem of characterizing the class of stationary distributions arising in such a model and give criteria for a d.f. to belong to it. We develop further results when the Zn's are random variables concentrated on the interval [0, 1], namely having a beta distribution.

Sign in / Sign up

Export Citation Format

Share Document