Reproduce “Continuous Time Markov Networks” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Tal El-Hay, Nir Friedman, Daphne Koller, Raz Kupferman
The authors introduce continuous-time Markov networks (CTMNs), motivated by protein mutations whose survival depends on how the whole sequence functions. CTMNs separate state-independent local change proposals from acceptance based on a structured fitness distribution. Under a reciprocal-ratio condition, that distribution is the process’s stationary distribution. In synthetic tests, CTMN and CTBN learning handle non-equilibrium starts; protein evolution is not tested.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.