Continuous-Time Markov Chain–Based Flux Analysis in Metabolism

Continuous Time Markov Chains

Discrete-time Markov chains are useful in simulation, since updating algorithms are easier to construct in discrete steps. They can also be useful as crude models of physical, biological, and social processes. However, in the physical and biological worlds time runs continuously, and so discrete-time mathematical models are not always appropriate. This is especially true in population biology –...

Continuous Time Markov Networks

A central task in many applications is reasoning about processes that change over continuous time. Recently, Nodelman et al. introduced continuous time Bayesian networks (CTBNs), a structured representation for representing Continuous Time Markov Processes over a structured state space. In this paper, we introduce continuous time Markov networks (CTMNs), an alternative representation language t...

Continuous-time Markov Chains

Bayesian Analysis of Elapsed Times in Continuous-Time Markov Chains

We explore Bayesian analysis for continuous-time Markov chain (CTMC) models based on a conditional reference prior. For CTMC models, inference of the elapsed time between chain observations depends heavily on the rate of decay of the prior as the elapsed time increases. Moreover, improper priors on the elapsed time may lead to improper posterior distributions. In addition to the elapsed time, a...

Continuous-time homogeneous Markov chains