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Found 4 papers, 3 papers with code
Inference in conditioned dynamics through causality restoration
Computing observables from conditioned dynamics is typically computationally hard, because, although obtaining independent samples efficiently from the unconditioned dynamics is usually feasible, generally most of the samples must be discarded (in a form of importance sampling) because they do not satisfy the imposed conditions.
A Bayesian generative neural network framework for epidemic inference problems
The reconstruction of missing information in epidemic spreading on contact networks can be essential in the prevention and containment strategies.
Epidemic mitigation by statistical inference from contact tracing data
We conclude that probabilistic risk estimation is capable to enhance performance of digital contact tracing and should be considered in the currently developed mobile applications.
Loop corrections in spin models through density consistency
Computing marginal distributions of discrete or semidiscrete Markov random fields (MRFs) is a fundamental, generally intractable problem with a vast number of applications in virtually all fields of science.
