Benchmark estimation is motivated by the goal of producing an approximation to a posterior distribution that is better than the empirical distribution function. This is accomplished by incorporating additional information into the construction of the approximation. We focus here on generalized poststratification, the most successful implementation of benchmark estimation in our experience. We d...

The standard method for training Hidden Markov Models optimizes a point estimate of the model parameters. This estimate, which can be viewed as the maximum of a posterior probability density over the model parameters, may be susceptible to over-tting, and contains no indication of parameter uncertainty. Also, this maximummay be unrepresentative of the posterior probability distribution. In this...

The viewpoint taken in this paper is that data assimilation is fundamentally a statistical problem and that this problem should be cast in a Bayesian framework. In the absence of model error, the correct solution to the data assimilation problem is to find the posterior distribution implied by this Bayesian setting. Methods for dealing with data assimilation should then be judged by their abili...

Variational auto-encoders (VAE) are scalable and powerful generative models. However, the choice of the variational posterior determines tractability and flexibility of the VAE. Commonly, latent variables are modeled using the normal distribution with a diagonal covariance matrix. This results in computational efficiency but typically it is not flexible enough to match the true posterior distri...

at 30 minutes after intravenous administration of the glomerular renal agent tc-99m-dtpa, both right and left lateral views were obtained. we analyzed the ratio of optical densities (behind the ureter/in front of the ureter). in patients without gross renal failure or retroperitoneal disease, the ratio was always less than 1 (range 0.38 to 0.95, mean 0.68). this represents greater perfusion of ...

objective: in all ceramic fixed partial dentures the connector area is a common fracture location. the survival time of three-unit fixed partial dentures may be improved by altering the connector design in regions of maximum tension. the purpose of this study was to determine the effect of buccolingual increase of the connector width on the stress distribution in posterior fixed partial denture...

We consider a logistic regression model with a Gaussian prior distribution over the parameters. We show that an accurate variational transformation can be used to obtain a closed form approximation to the posterior distribution of the parameters thereby yielding an approximate posterior predictive model. This approach is readily extended to binary graphical model with complete observations. For...

In Bayesian estimation the posterior distribution is proportional to likelihood function and the prior density of the parameter. Thus Bayesian inference, most often, is affected by the prior density. In this paper we look at how we can make Bayesian inference more robust against a poorly specified prior. We find that using a mixture of conjugate priors enables us to do this. We allow a small pr...

The Markov Chain Monte Carlo (MCMC) technique provides a means to generate a random sequence of model realizations that sample the posterior probability distribution of a Bayesian analysis. That sequence may be used to make inferences about the model uncertainties that derive from measurement uncertainties. This paper presents an approach to improving the eeciency of the Metropolis approach to ...

In this paper, we propose a Bayesian approach for affine auto-calibration. By the Bayesian approach, a posterior distribution for the affine camera parameters can be constructed, where also the prior knowledge can be taken into account. Moreover, due to the linearity of the affine camera model, the structure and translations can be analytically marginalised out from the posterior distribution, ...