Consider the simple setting of point exposure, outcome and confounding variables, all of which are discrete. As is well known, parametric modeling of outcome given exposure and confounders and also exposure given confounders can yield a double-robust estimator. This has the property of being consistent as long as at least one of the two specified models is correct. Such an estimator can also be...

In order to estimate the motion of an object, the visual system needs to combine multiple local measurements , each of which carries some degree of ambiguity. We present a model of motion perception whereby measurements from diierent image regions are combined according to a Bayesian estimator | the estimated motion maximizes the posterior probability assuming a prior favoring slow and smooth v...

This paper investigates nonparametric estimation of density on [0,1]. The kernel estimator of density on [0,1] has been found to be sensitive to both bandwidth and kernel. This paper proposes a unified Bayesian framework for choosing both the bandwidth and kernel function. In a simulation study, the Bayesian bandwidth estimator performed better than others, and kernel estimators were sensitive ...

We consider parallel global optimization of derivative-free expensive-to-evaluate functions, and proposes an efficient method based on stochastic approximation for implementing a conceptual Bayesian optimization algorithm proposed by [10]. To accomplish this, we use infinitessimal perturbation analysis (IPA) to construct a stochastic gradient estimator and show that this estimator is unbiased.

The horseshoe+ estimator for Gaussian linear regression models is a novel extension of the horseshoe estimator that enjoys many favourable theoretical properties. We develop the first efficient Gibbs sampling algorithm for the horseshoe+ estimator for linear and logistic regression models. Importantly, our sampling algorithm incorporates robust data models that naturally handle non-Gaussian dat...

This paper studies a 2×2 static game of incomplete information. I allow players’ private signals to be correlated, which adds complexity to Bayesian Nash Equilibrium (BNE) solutions of the game. Further, the econometric structure of this model is “incomplete” (Tamer, 2003). I therefore focus on a nontrivial subset, Π2(θ0), of the support of public information variables where a unique Monotone S...

The traditional Bayesian estimator of short-time spectral amplitude is based on the minimization of the squared-error cost function under the common Gaussian probability density function (pdf). The Gaussian distribution, however, is not the optimal probability distribution. To overcome this phenomenon, we considered to replace the traditional distribution hypothesis of spectral amplitude of spe...

Real world Speaker Identification (SI) application differs from ideal or laboratory conditions causing perturbations that leads to a mismatch between the training and testing environment and degrade the performance drastically. Many strategies have been adopted to cope with acoustical degradation; wavelet based Bayesian marginal model is one of them. But Bayesian marginal models cannot model th...