Reconstructing 3D motion data is highly under-constrained due to several common sources of data loss during measurement, such as projection, occlusion, or miscorrespondence. We present a statistical model of 3D motion data, based on the Kronecker structure of the spatiotemporal covariance of natural motion, as a prior on 3D motion. This prior is expressed as a matrix normal distribution, compos...

The common prior assumption is pervasive in game-theoretic models with incomplete information. This paper investigates experimentally the importance of inducing a common prior in a two-person signaling game. For a specific probability distribution of the sender’s type, the long-run behavior without an induced common prior is shown to be different from the behavior when a common prior is induced...

Methods AIS patients with age over 10 years, Risser sign of 0 to II, within one year postmenarche, Cobb angle of 41 to 50 degrees before treatment and underwent no prior treatment were included in the study. At the final follow-up after the patients reached skeletal maturity, the rate of the patients whose curve was stabilized by the treatment (the curve had not progressed in more than 6 degree...

The problem of choosing a prior distribution for the Bayesian interpretation of measurements (specifically internal dosimetry measurements) is considered using a theoretical analysis and by examining historical tritium and plutonium urine bioassay data from Los Alamos. Two models for the prior probability distribution are proposed: (1) the log-normal distribution, when there is some additional ...

We consider a change detection problem in which the arrival rate of a Poisson process changes suddenly at some unknown and unobservable disorder time. It is assumed that the prior distribution of the disorder time is known. The objective is to detect the disorder time with an online detection rule (a stopping time) in a way that balances the frequency of false alarm and detection delay. So far ...

A new approach to Bayesian reconstruction is proposed that endows the prior probability distribution with an inherent geometrical flexibility, which is achieved through a transformation of the coordinate system of the prior distribution or model into that of the reconstruction. With this warping, prior morphological information regarding the object that is being reconstructed may be adapted to ...

We expand a framework for Bayesian variable selection for Gaussian process (GP) models by employing spiked Dirichlet process (DP) prior constructions over set partitions containing covariates. Our approach results in a nonparametric treatment of the distribution of the covariance parameters of the GP covariance matrix that in turn induces a clustering of the covariates. We evaluate two prior co...

We consider Bayesian inference in semiparametric mixed models (SPMMs) for longitudinal data. SPMMs are a class of models that use a nonparametric function to model a time effect, a parametric function to model other covariate effects, and parametric or nonparametric random effects to account for the within-subject correlation. We model the nonparametric function using a Bayesian formulation of ...

although filling the gap in reading comprehension gained momentum with the rise of the top-down approach, vygotsky’ concept of scaffolding and the dual code  theory provided a strong support for the use of paratext to enhance comprehension. scaffolding is dependent on other-regulation, one type of which is object-regulation. from this vantage-point, various types of paratext can function as sou...