On aggregated Markov processes

1986/03/01 by Donald R. Fredkin, D. R. Fredkin, John A. Rice · 9 citations
Biochemistry, Genetics and Molecular Biology · Neuroscience · #Receptor Mechanisms and Signaling #Neural dynamics and brain function #Neurotransmitter Receptor Influence on Behavior

paper · doi:10.2307/3214130

Abstract

A finite-state Markov process is aggregated into several groups. What can be learned about the underlying process from the aggregated one? We provide some partial answers to this question.

Cited by

• Computing rates of Markov models of voltage-gated ion channels by inverting partial differential equations governing the probability density functions of the conducting and non-conducting states
• Modelling ion channels with a view towards identifiability
• Analysis and control of a multi-layer time-varying SIS model with an infrastructure network
• The biophysics of peptide models of ion channels
• Learning Parametric-Output HMMs with Two Aliased States
• Inositol trisphosphate receptor and ion channel models based on single-channel data. [europepmc]
• A data-driven model of a modal gated ion channel: the inositol 1,4,5-trisphosphate receptor in insect Sf9 cells. [europepmc]
• Decoding Single Molecule Time Traces with Dynamic Disorder. [europepmc]
• Modelling ion channels with a view towards identifiability. [europepmc]

Related

• Applications of the Theory of Aggregated Markov Processes in Stochastic Learning Theory
• Exact and ordinary lumpability in finite Markov chains
• On Determining and Qualifying the Number of Superstates in Aggregation of Markov Chains
• Analysis of structured Markov processes
• Markov chain aggregation and its applications to combinatorial reaction\n networks