Add to ORKGConsider a network of n compartments assuming that transition processes may occur between them: e.g. heat or mass transfer, or any kind of exchange of the first order. In fact, we have a Markov process with n states
AbstractA number of important theorems arising in connection with Gaussian elimination are derived, ...
Finite Markov chains are probabilistic network models that are commonly used as representations of d...
Based upon the Grassman, Taksar and Heyman algorithm [1] and the equivalent Sheskin State Reduction ...
Consider a network of n compartments assuming that transition processes may occur betwee...
A molecular model is used for describing diffusion and sorption of a single component in a one-dimen...
Linear compartmental models are commonly used in different areas of science, particularly in modelin...
Linear compartmental models are commonly used in different areas of science, particularly in modelin...
Complex Markov models is a widely used and powerful predictive tool to analyze stochastic biochemica...
Diffusion models arising in analysis of large biochemical models and other complex systems are typic...
We propose a model of Markovian quantity flows on connected networks that relaxes several properties...
Specifications for stochastic Markov-renewal (MR) models are compared with those for deterministic, ...
Accurate modeling and numerical simulation of reaction kinetics is a topic of steady interest. We co...
AbstractLet Y={Yt:t⩾0} be a semi-Markov process whose state space S is finite. Assume that Y is eith...
This dissertation presents a theoretical study of arbitrary discretizations of general nonequilibriu...
AbstractThe differential equations for transient state probabilities for Markovian processes are exa...
AbstractA number of important theorems arising in connection with Gaussian elimination are derived, ...
Finite Markov chains are probabilistic network models that are commonly used as representations of d...
Based upon the Grassman, Taksar and Heyman algorithm [1] and the equivalent Sheskin State Reduction ...
Consider a network of n compartments assuming that transition processes may occur betwee...
A molecular model is used for describing diffusion and sorption of a single component in a one-dimen...
Linear compartmental models are commonly used in different areas of science, particularly in modelin...
Linear compartmental models are commonly used in different areas of science, particularly in modelin...
Complex Markov models is a widely used and powerful predictive tool to analyze stochastic biochemica...
Diffusion models arising in analysis of large biochemical models and other complex systems are typic...
We propose a model of Markovian quantity flows on connected networks that relaxes several properties...
Specifications for stochastic Markov-renewal (MR) models are compared with those for deterministic, ...
Accurate modeling and numerical simulation of reaction kinetics is a topic of steady interest. We co...
AbstractLet Y={Yt:t⩾0} be a semi-Markov process whose state space S is finite. Assume that Y is eith...
This dissertation presents a theoretical study of arbitrary discretizations of general nonequilibriu...
AbstractThe differential equations for transient state probabilities for Markovian processes are exa...
AbstractA number of important theorems arising in connection with Gaussian elimination are derived, ...
Finite Markov chains are probabilistic network models that are commonly used as representations of d...
Based upon the Grassman, Taksar and Heyman algorithm [1] and the equivalent Sheskin State Reduction ...