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In this note we show a simple formula for the joint density of local times, last exit tree and cycling numbers of continuous-time Markov Chains on finite graphs, which involves the modified Bessel function of the first type.

Probability · Mathematics 2018-03-28 Ruojun Huang , Daniel Kious , Vladas Sidoravicius , Pierre Tarrès

Let $X$ be the constrained random walk on ${\mathbb Z}_+^2$ having increments $(1,0)$, $(-1,1)$, $(0,-1)$ with jump probabilities $\lambda(M_k)$, $\mu_1(M_k)$, and $\mu_2(M_k)$ where $M$ is an irreducible aperiodic finite state Markov…

Probability · Mathematics 2019-09-17 Fatma Başoğlu Kabran , Ali Devin Sezer

Random walks (or Markov chains) are models extensively used in theoretical computer science. Several tools, including analysis of quantities such as hitting and mixing times, are helpful for devising randomized algorithms. A notable example…

Quantum Physics · Physics 2023-07-12 Lorenzo Laneve , Francesco Tacchino , Ivano Tavernelli

We consider the piecewise-deterministic Markov process obtained by randomly switching between the flows generated by a finite set of smooth vector fields on a compact set. We obtain H\"ormander-type conditions on the vector fields…

Probability · Mathematics 2023-02-14 Michel Benaïm , Oliver Tough

We investigate the local times of a continuous-time Markov chain on an arbitrary discrete state space. For fixed finite range of the Markov chain, we derive an explicit formula for the joint density of all local times on the range, at any…

Probability · Mathematics 2009-09-29 David Brydges , Remco van der Hofstad , Wolfgang König

We study the existence of densities for distributions of piecewise deterministic Markov processes. We also obtain relationships between invariant densities of the continuous time process and that of the process observed at jump times. In…

Probability · Mathematics 2020-06-03 Piotr Gwiżdż , Marta Tyran-Kamińska

In this paper, we are concerned with centered Markov Additive Processes $\{(X_t,Y_t)\}_{t\in\T}$ where the driving Markov process $\{X_t\}_{t\in\T}$ has a finite state space. Under suitable conditions, we provide a local limit theorem for…

Probability · Mathematics 2013-06-25 Loïc Hervé , James Ledoux

New results on conditional joint probability distributions of first exit times are presented for a continuous-time stochastic process defined as the mixture of Markov jump processes moving at different speeds on the same finite state space,…

Probability · Mathematics 2018-09-19 B. A. Surya

Let be $(X_t, t\geq 0)$ be a L\'evy process which is the sum of a Brownian motion with drift and a compound Poisson process. We consider the first passage time $\tau_x$ at a fixed level $x>0$ by $(X_t, t\geq 0)$ and $K_x:= X_{\tau_x}-x$ the…

Probability · Mathematics 2016-03-09 Laure Coutin , Waly Ngom

We study the random acceleration model, which is perhaps one of the simplest, yet nontrivial, non-Markov stochastic processes, and is key to many applications. For this non-Markov process, we present exact analytical results for the…

Statistical Mechanics · Physics 2019-09-04 Satya N. Majumdar , Alberto Rosso , Andrea Zoia

In this note we derive the exact probability that a specific state in a two-state Markov chain is visited exactly $k$ times after $N$ transitions. We provide a closed-form solution for $\mathbb{P}(N_l = k \mid N)$, considering initial state…

Probability · Mathematics 2025-02-07 Mohammad Taha Shah

We describe an exact approach for calculating transition probabilities and waiting times in finite-state discrete-time Markov processes. All the states and the rules for transitions between them must be known in advance. We can then…

Other Condensed Matter · Physics 2009-11-11 Semen A. Trygubenko , David J. Wales

We investigate the local times of a continuous-time Markov chain on an arbitrary discrete state space. For fixed finite range of the Markov chain, we derive an explicit formula for the joint density of all local times on the range, at any…

Probability · Mathematics 2007-05-23 D. Brydges , R. van der Hofstad , W. Konig

We consider a piecewise-deterministic Markov process governed by a jump intensity function, a rate function that determines the behaviour between jumps, and a stochastic kernel describing the conditional distribution of jump sizes. We study…

Probability · Mathematics 2010-09-22 K. A. Borovkov , G. Last

We propose a method based on continuous time Markov chain approximation to compute the distribution of Parisian stopping times and price Parisian options under general one-dimensional Markov processes. We prove the convergence of the method…

Computational Finance · Quantitative Finance 2021-07-15 Gongqiu Zhang , Lingfei Li

This paper considers population processes in which general, not necessarily Markovian, multivariate Hawkes processes dictate the stochastic arrivals. We establish results to determine the corresponding time-dependent joint probability…

Probability · Mathematics 2021-06-08 Raviar Karim , Roger J. A. Laeven , Michel Mandjes

It is shown that large deviation statistical quantities of the discrete time, finite state Markov process $P_{n+1}^{(j)}=\sum_{k=1}^NH_{jk}P_n^{(k)}$, where P_n^{(j)} is the probability for the j-state at the time step n and H_{jk} is the…

Chaotic Dynamics · Physics 2009-11-13 Miki U. Kobayashi , Hirokazu Fujisaka , Syuji Miyazaki

We consider here point processes $N^f(t)$, $t>0$, with independent increments and integer-valued jumps whose distribution is expressed in terms of Bern\v{s}tein functions $f$ with L\'evy measure $\nu$. We obtain the general expression of…

Probability · Mathematics 2014-10-31 Enzo Orsingher , Bruno Toaldo

We consider a continuous-time Markov chain with a finite or countable state space. For a site y and subset H of the state space, the hitting time of y under taboo H is defined to be infinite if the process trajectory hits H before y, and…

Probability · Mathematics 2013-11-25 Ekaterina Vl. Bulinskaya

We study the problem of characterizing the expected hitting times for a robust generalization of continuous-time Markov chains. This generalization is based on the theory of imprecise probabilities, and the models with which we work…

Probability · Mathematics 2022-06-28 Thomas Krak
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