Consider a continuous time, finite state Markov chain X = {Xt, t ∈ [0, T ]}. We identify the states of this process with the unit vectors ei in R N , where N is the number of states of the chain. We consider stochastic processes defined on the filtered probability space (Ω, F , {Ft}, P), where {Ft} is the completed natural filtration generated by the σ-fields Ft = σ({Xu, u ≤ t}, F ∈ FT : P(F ) ...

Rough volatility models have recently been empirically shown to provide a good fit historical time series and implied smiles of SPX options. They are continuous-time stochastic models, whose process is driven by fractional Brownian motion with Hurst parameter less than half. Due the challenge that it neither semimartingale nor Markov process, there no unified method not only applies all rough b...

Markov-reward models, as extensions of continuous-time Markov chains, have received increased attention for the specification and evaluation of performance and dependability properties of systems. Until now, however, the specification of reward-based performance and dependability measures has been done manually and informally. In this paper, we change this undesirable situation by the introduct...

We study new formulae based on Lyapunov exponents for entropy, mutual information, and capacity of finite state discrete time Markov channels. We also develop a method for directly computing mutual information and entropy using continuous state space Markov chains. Our methods allow for arbitrary input processes and channel dynamics, provided both have finite memory. We show that the entropy ra...

in this paper, absorbing markov chain models are developed to determine the optimum process mean levels for both a single-stage and a serial two-stage production system in which items are inspected for conformity with their specification limits. when the value of the quality characteristic of an item falls below a lower limit, the item is scrapped. if it falls above an upper limit, the item is ...

Personal financial decision making plays an important role in modern finance. Decision problems about consumption and insurance are modelled in a continuous-time multi-state Markovian framework. The optimal solution is derived and studied. The model, the problem, and its solution are exemplified by two special cases: In one model the individual takes optimal positions against the risk of dying;...

Abstract Continuous time Markov chains are often used in the literature to model the dynamics of a system with low species count and uncertainty in transitions. In this paper, we investigate three particular algorithms that can be used to numerically simulate continuous time Markov chain models (a stochastic simulation algorithm, explicit and implicit tau-leaping algorithms). To compare these m...