On the Definition of a Strong Markov Process

1960 ◽  
Vol 5 (2) ◽  
pp. 216-220
Author(s):  
A. A. Yushkevich
Keyword(s):  
Markov Process ◽  
Strong Markov Process ◽  
Definition Of ◽  
Strong Markov

scholarly journals Excision of a strong Markov process

1972 ◽  
Vol 23 (2) ◽  
pp. 114-120 ◽  
Author(s):  
F. B. Knight ◽  
A. O. Pittenger
Keyword(s):  
Markov Process ◽  
Strong Markov Process ◽  
Strong Markov

scholarly journals Parametrix construction for certain Lévy-type processes

2015 ◽  
Vol 0 (0) ◽  
Author(s):  
Victoria Knopova ◽  
Alexei Kulik
Keyword(s):  
Markov Process ◽  
Probability Density ◽  
Lower Bounds ◽  
Transition Probability ◽  
Local Operator ◽  
Upper And Lower Bounds ◽  
Transition Probability Density ◽  
Strong Markov Process ◽  
Non Local ◽  
Strong Markov

AbstractIn this paper, we show that a non-local operator of certain type extends to the generator of a strong Markov process, admitting the transition probability density. For this transition probability density we construct the intrinsic upper and lower bounds, and prove some smoothness properties. Some examples are provided.

scholarly journals Quasi-Regular Dirichlet Forms: Examples and Counterexamples

1995 ◽  
Vol 47 (1) ◽  
pp. 165-200 ◽  
Author(s):  
Michael Röckner ◽  
Byron Schmuland
Keyword(s):  
Markov Process ◽  
Dirichlet Forms ◽  
Loop Spaces ◽  
Strong Markov Process ◽  
Speed Measure ◽  
Strong Markov

AbstractWe prove some new results on quasi-regular Dirichlet forms. These include results on perturbations of Dirichlet forms, change of speed measure, and tightness. The tightness implies the existence of an associated right continuous strong Markov process. We also discuss applications to a number of examples including cases with possibly degenerate (sub)-elliptic part, diffusions on loop spaces, and certain Fleming- Viot processes.

Two extreme value processes arising in hydrology

1976 ◽  
Vol 13 (1) ◽  
pp. 190-194 ◽  
Author(s):  
Alan F. Karr
Keyword(s):  
Markov Process ◽  
Random Measure ◽  
Extreme Value ◽  
Poisson Random Measure ◽  
One Dimensional ◽  
Flood Peak ◽  
Base Level ◽  
Strong Markov Process ◽  
Hydrological System ◽  
Strong Markov

Let Tn be the time of occurrence of the nth flood peak in a hydrological system and Xn the amount by which the peak exceeds a base level. We assume that ((Tn, Xn)) is a Poisson random measure with mean measure μ(dx) K(x, dy). In this note we characterize two extreme value processes which are functionals of ((Tn, Xn)). The set-parameterized process {MA} defined by MA = sup {Xn:Tn ∈ A} is additive and we compute its one-dimensional distributions explicitly. The process (Mt), where Mt = sup{Xn: Tn ≦ t}, is a non-homogeneous strong Markov process. Our results extend but computationally simplify those of previous models.

Two extreme value processes arising in hydrology

1976 ◽  
Vol 13 (01) ◽  
pp. 190-194 ◽  
Author(s):  
Alan F. Karr
Keyword(s):  
Markov Process ◽  
Random Measure ◽  
Extreme Value ◽  
Poisson Random Measure ◽  
One Dimensional ◽  
Flood Peak ◽  
Base Level ◽  
Strong Markov Process ◽  
Hydrological System ◽  
Strong Markov

Let Tn be the time of occurrence of the nth flood peak in a hydrological system and Xn the amount by which the peak exceeds a base level. We assume that ((Tn , Xn )) is a Poisson random measure with mean measure μ(dx) K(x, dy). In this note we characterize two extreme value processes which are functionals of ((Tn , Xn )). The set-parameterized process {MA } defined by MA = sup {Xn :Tn ∈ A} is additive and we compute its one-dimensional distributions explicitly. The process (Mt ), where Mt = sup{Xn : Tn ≦ t}, is a non-homogeneous strong Markov process. Our results extend but computationally simplify those of previous models.

The Proof of Strong Markov Property Based on one Definition

2015 ◽  
Vol 742 ◽  
pp. 419-428
Author(s):  
Rong Tang ◽  
Yi Xuan Dong
Keyword(s):  
Markov Process ◽  
Markov Property ◽  
Subject Classification ◽  
Strong Markov Property ◽  
Homogeneous Markov Process ◽  
Primary 60J25 ◽  
Strong Markov Process ◽  
2000 Mathematics Subject Classification ◽  
Strong Markov

In this paper, for countable homogeneous Markov process, we prove strong Markov property defining by [2] are valid. So for an arbitrary countable homogeneous Markov process is a strong Markov process.2000 Mathematics Subject Classification. Primary 60J25, 60J27.

scholarly journals Entropic Equilibria Selection of Stationary Extrema in Finite Populations

Entropy ◽  
2018 ◽  
Vol 20 (9) ◽  
pp. 631
Author(s):  
Marc Harper ◽  
Dashiell Fryer
Keyword(s):  
Markov Process ◽  
Population Size ◽  
Mutation Rate ◽  
Stationary Distribution ◽  
Markov Processes ◽  
Local Maxima ◽  
Moran Process ◽  
The Stability ◽  
Definition Of ◽  
Selection Of

We propose the entropy of random Markov trajectories originating and terminating at the same state as a measure of the stability of a state of a Markov process. These entropies can be computed in terms of the entropy rates and stationary distributions of Markov processes. We apply this definition of stability to local maxima and minima of the stationary distribution of the Moran process with mutation and show that variations in population size, mutation rate, and strength of selection all affect the stability of the stationary extrema.

Sign in / Sign up

Export Citation Format

Share Document