A random sample of curves can be usually thought of as noisy realisations of a compound stochastic process X(t) = Z{W(t)}, where Z(t) produces random amplitude variation and W(t) produces random ...
In certain multivariate problems the full probability density has an awkward normalizing constant, but the conditional and/or marginal distributions may be much more tractable. In this paper we ...
We present a maximum-likelihood method for parameter estimation in terahertz time-domain spectroscopy. We derive the likelihood function for a parameterized frequency response function, given a pair ...
Our method can be used to train implicit probabilistic models (a common example being the generator in GANs). Unlike GANs, however, our method does not suffer from mode collapse/dropping and is stable ...
Our method can be used to train implicit probabilistic models (a common example being the generator in GANs). Unlike GANs, however, our method does not suffer from mode collapse/dropping and is stable ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results