We derive a thin-film model for viscoelastic liquids under strong slip which obey the stress tensor dynamics of corotational Jeffreys fluids.

The network autocorrelation model has been extensively used by researchers interested modeling social influence effects in social networks. The most common inferential method in the model is classical maximum likelihood estimation. This approach, however, has known problems such as negative bias of the network autocorrelation parameter and poor coverage of confidence intervals. In this paper, w...

We consider efficient Bayesian network structure learning (BNSL) based on scores using branch and bound. Thus far, as a BNSL score, the Bayesian Dirichlet equivalent uniform (BDeu) has been used most often, but it is recently proved that the BDeu does not choose the simplest model even when the likelihood is maximized whereas Jeffreys’ prior and MDL satisfy such regularity. Although the BDeu ha...

where Z is the partition function Z( ) = R exp( x) dQx, and can := f j Z( ) < 1g is the canonical parameter space. We let sup = supf j 2 cang, and inf likewise. The elements of the exponential family are also parametrized by their mean value . We write for the mean value corresponding to the canonical parameter and for the canonical parameter corresponding to the mean value : For any x the maxi...

A refinement of the discrete Jensen’s inequality for convex functions defined on a convex subset in linear spaces is given. Application for f -divergence measures including the Kullback-Leibler and Jeffreys divergences are provided as well.

Posterior distributions are useful for a broad range of tasks in machine learning ranging from model selection to reinforcement learning. Given that modern machine learning models can have millions of parameters, selecting an informative prior is typically infeasible, resulting in widespread use of priors that avoid strong assumptions. For example, recent work on deep generative models (Kingma ...

Jeffreys (1946) proposed a root-information prior for the likelihood analysis of a statistical model. Then for an exponential model with scalar parameter Welch & Peers (1963) showed that this leads to second-order inference, or more specifically that posterior intervals have second order confidence. But for an exponential model with vector-parameter Jeffreys (1961) found that the root informati...