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Found 3 papers, 2 papers with code
Bayesian posterior approximation with stochastic ensembles
We introduce ensembles of stochastic neural networks to approximate the Bayesian posterior, combining stochastic methods such as dropout with deep ensembles.
Stochastic unfolding of nanoconfined DNA: experiments, model and Bayesian analysis
The estimated friction coefficient is in agreement with theoretical estimates for motion of a cylinder in a channel.
Biological Physics Mesoscale and Nanoscale Physics
Spectral Statistics in Chaotic Systems with Two Identical Connected Cells
Its only free parameter is the characteristic time of exchange between the cells in units of the Heisenberg time.
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