Bayesian estimation and maximum likelihood methods represent two central paradigms in modern statistical inference. Bayesian estimation incorporates prior beliefs through Bayes’ theorem, updating ...

Journal of the Royal Statistical Society. Series B (Statistical Methodology), Vol. 63, No. 2 (2001), pp. 243-259 (17 pages) The analysis of a sample of curves can be done by self-modelling regression ...

The Annals of Statistics, Vol. 21, No. 3 (Sep., 1993), pp. 1567-1590 (24 pages) We study in detail asymptotic properties of maximum likelihood estimators of parameters when observations are taken from ...

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 ...

A brief description of the methods used by the SYSLIN procedure follows. For more information on these methods, see the references at the end of this chapter. There are two fundamental methods of ...

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 ...