Integrating Markov processes with structural causal modeling enables counterfactual inference in complex systems.
Robert Osazuwa NessKaushal PaneriOlga VitekPublished in: CoRR (2019)
Keyphrases
- complex systems
- markov processes
- random fields
- dynamic systems
- markov process
- stochastic processes
- markov chain
- continuous time bayesian networks
- biological systems
- multi agent systems
- non stationary
- dynamic bayesian networks
- discrete event systems
- physical systems
- bayesian networks
- continuous time markov chains
- probabilistic inference
- steady state simulation
- belief networks
- markov chain monte carlo
- markov random field
- parameter estimation
- random walk